Self-improving classification system

ABSTRACT

A self-improving classification system classifies specimens based on class identifiers. The system stores specimen profiles in a database that is updated with additional specimen profiles and with follow-up data that corrects classification of specimens that were initially incorrectly classified. Algorithms use the updated database to discover new class identifiers, modify thresholds of known class identifiers, and drop unnecessary class identifiers to improve classification of specimens.

This application claims the benefit of U.S. Provisional Application No. 60/582,352 filed on Jun. 23, 2004, for “Self-Improving Classification System” by O. Soykan.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to applications entitled “Self-Improving Identification Method”, “Genetic Diagnostic Method for SCD Risk Stratification” and “Assessing Patient Risk with Biochemical Markers,” which were filed on the same day and also assigned to Medtronic, Inc.

BACKGROUND OF THE INVENTION

The present invention relates to a system and method for classifying biological specimens. In particular, the present invention relates to building and improving a system and method for classifying biological specimens.

Classification of biological specimens has broad applications in the healthcare and research fields. Its uses range from diagnosing whether or not a patient has a disease, to determining which therapy will work for a particular patient, to determining the subclass of a tumor or microorganism. Though classification is performed routinely, it is often a difficult and imprecise process. Diagnosis of patients with incomplete symptoms or partially penetrated phenotypes, for instance, is a common, but difficult, problem.

For example, there are multiple tests to identify patients who are susceptible to life threatening arrhythmias. However, none of these tests have the desired specificity and sensitivity to reliably identify all patients at risk. As a result, many of these patients do not receive optimal therapy, and lives are lost every year.

Current diagnostic or classification techniques utilize symptoms reported by patients, the presence or measurement of biological and physiological markers, and the experience and intuition of the healthcare provider. In order to make classification more reliable, researchers have tried to identify new biological and physiological markers. Current scientific methods for discovering new markers require previous knowledge of the biochemical pathway for a particular disease or process. In addition, only one marker can be studied at a time—making it a difficult, expensive, and lengthy process.

It is likely that many indicators associated with each disease or condition exist, but medical knowledge has not yet hypothesized their roles in these pathways. Therefore, there is a need for an improved process for classifying biological specimens and identifying new indicators to continually improve the process.

BRIEF SUMMARY OF THE INVENTION

The present invention is a method and system for classifying specimens. An algorithm for classifying specimens is derived from class identifiers. Specimens are classified by analysis of profiles generated for each specimen. If it is subsequently determined that a specimen is incorrectly classified, that specimen profile is reclassified. The algorithm is then refined based on data from the reclassified specimen profile.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a representative embodiment of the present invention.

FIG. 2 is a flowchart of an algorithm to generate and update a class table.

FIG. 3 is a flowchart of an algorithm to refine class identifiers.

FIG. 4 is a conceptual diagram of a representative embodiment of the present invention.

FIG. 5 is a logical diagram of a representative classification algorithm.

FIG. 6 is a graph showing protein spectra.

FIG. 7 is logical diagram illustrating a technique for identifying class identifiers.

FIG. 8 is a logical diagram of a representative classification algorithm.

FIG. 9 is a logical diagram of a representative classification algorithm.

FIG. 10 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs1008832.

FIG. 11 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs2238043.

FIG. 12 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs198544.

FIG. 13 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs1009531.

FIG. 14 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs2121081.

FIG. 15 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs1428568.

FIG. 16 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs918050.

FIG. 17 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs1483312.

FIG. 18 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs1859037.

FIG. 19 is a mosaic plot illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNP rs6964587.

FIG. 20 is a graph illustrating the probability of experiencing fatal VT/VF as a function of allele specific inheritance of SNPs rs1483312 and rs2238043.

DESCRIPTION

The Self-Improving Classification Process (FIG. 1) The present invention is a system and method to build and implement a process that can be used to classify any biological specimen. Specimens can be a person, an animal, a plant, a tissue sample, a bodily fluid sample, cells, subcellular factors, and microorganisms. Specimens can be sorted into a class based on any type of distinction as well. Examples of class distinctions are risk, diagnosis, or prognosis of a disease or condition (for example, diabetes); the ideal therapy against a condition; the ideal route of administering a therapy; whether there would be a benefit from therapy (either drug or medical device therapy); recovery from a condition; the class of a tumor; the specific type of an infection; etc.

FIG. 1 is a flowchart of classification process 10, which is a representative embodiment of the present invention. Process 10 begins with initial population of specimens 12, which are specimens that are of known classification.

At step 14, specimen information and data are obtained from initial population of specimens 12. The information and data can be of any type and will be described in more detail below.

At step 16, specimen profiles are generated for each specimen of the initial population. The specimen profiles are based on the information and data obtained from the specimens. The classification of the specimen profile and specimen, based on prior knowledge, is established. The specimen profile and classification is then stored in a database at step 18.

Next, at step 20, a class table is generated. The class table is a vector where each entry represents the number of specimens with a given condition. The class table does not specify which specimens belong to each class, but instead, provides comprehensive information of the specimen population (e.g. the frequency of a given disease in the general public). For example, a vector of [6,45,76, . . . ] means that 6 specimens have condition 1, 45 specimens have condition 2, 76 specimens have condition 3, etc. The entries and number of entries in the class table are dynamic. If the distinctions of the class table are diseases, for example, the class table may also be referred to as a disease table.

The class table is subsequently used, at step 22, to refine class identifiers. Class identifiers are benchmarks that distinguish classes. Class identifiers are based on any type of information and data that is gathered from the specimens, such as proteomic, genetic, and lipidomic biological markers, as well as physiologic and demographic parameters. At this point, the class identifiers may be any combination of previously known and newly identified class identifiers. Refining class identifiers includes discovering new class identifiers, modifying thresholds of class identifiers, and dropping unnecessary class identifiers.

At step 24, an algorithm to classify specimens is derived or developed. The algorithm is based on the class identifiers.

The next part of process 10 begins with unclassified specimen 26. The classification of unclassified specimen 26 is unknown.

At step 28, specimen information and data is derived from unclassified specimen 26. A specimen profile is then generated at step 30.

The algorithm developed at step 24 is utilized at step 32 to classify unclassified specimen 26. The resulting classification along with the specimen profile, are stored in the database represented by step 18. The profile and classification of specimen 26 is used (along with other stored profiles and classifications in the database) to generate a class table (step 20), refine class identifiers (step 22), and derive an algorithm (step 24) as described previously. The means for deriving an algorithm, which includes updating and improving, is preferably a digital processor such as a computer.

The remaining part of process 10 begins with previously classified specimen 34. This is a specimen that was previously unclassified but has now been initially classified and has its profile and classification stored in the database.

At step 36, follow-up data, which includes any of the type of data and information that is described below, is obtained from previously classified specimen 34. The follow-up data can include, among other things, a correction to the specimen's classification. If the specimen was initially incorrectly classified, and it is later determined that the original classification was incorrect, the correct classification is obtained in the follow-up data.

The follow-up data is used to generate an updated specimen profile and classification at step 38. The updated specimen profile and classification is then stored in the database as shown by step 18. Alternatively, the specimen classification may not be entered into the database until after it is determined whether or not the initial classification was correct. Again, the updated and stored profile and classification is utilized in generating a class table (step 20), refining class identifiers (step 22), and deriving an algorithm (step 24) as described above.

Optionally, step 40 shows that the refined class identifiers from step 22 may be studied as targets for therapy of a given condition. This will be described in more detail below.

The algorithm of process 10 self-improves to increase the sensitivity (positive predictive value) and the specificity of process 10 and identifies new and/or more predictive classes. Utilizing the follow-up specimen information and data with any corrected classifications, as shown by step 36, are key to improving process 10. For example, a specimen is classified based on the specimen's profile, using the algorithm, as having a given condition. However, it is later determined that the specimen was incorrectly classified and does not have the given condition. If the information and data in the specimen profile is used to refine the algorithm without reclassifying the specimen, the algorithm will not be improved and will likely continue to incorrectly classify similar specimens. If, however, the specimen is reclassified, and the profile is utilized in refining the algorithm, the algorithm will improve and be less likely to incorrectly classify a similar specimen.

Reclassification will also correct any problems in the algorithm that are the result of a skewed initial population of specimens 12 that was used to initially generate the algorithm. For example, many of the specimens in initial population 12 having a given condition also, coincidently, have a given class identifier. This is picked up and utilized by the algorithm to distinguish those with the condition, while in reality the class identifier is not associated with the condition. Many subsequently classified specimens will be incorrectly classified using this algorithm. If these specimens are not reclassified when it is determined that the classification is not correct, the incorrect algorithm will be perpetuated. However, by reclassifying the specimens, the error based on the skewed initial population 12 is corrected. However, an initial skew in the data might help determine population specific class identifiers. For example, if an initial population was not balanced for gender or ethnic origins, then the initial class identifiers could be specific to the over-represented subgroups, which can increase the specificity of future tests. Thus, the present invention improves process 10 beyond just increasing the amount of data from which to derive an algorithm.

Class Table Generation (FIG. 2)

FIG. 2 is an algorithm, detailing step 20, for generating a class table. Generating the class table includes updating the class table. The means for generating the class table is preferably a digital processor. Here, N represents the total number of specimens in the database, and M represents a given class.

To construct the class table, the processor starts at step 42. At step 44, the total number of specimens in the database is determined, the class table is null, and no specimens are being considered.

The first specimen is then considered at step 46 along with the first class in step 48. If the first specimen belongs in the first class, then one is added to the first position of the vector, as indicated in step 50, to show that the number of specimens in class M has increased by 1.

At step 52, the processor considers whether all of the classes have been tracked for the first specimen. If more classes must be considered, the processor returns to step 48 and considers the next class. If all the classes have been considered for the first specimen, the processor continues to step 54.

At step 54, the processor considers whether the total number of specimens in the database has been considered. If more specimens must be considered, the processor returns to step 46 and considers the second specimen. If all specimens have been considered, the processor proceeds to step 56, where the class table is saved.

As the database is updated with more specimens and with corrected classifications of specimens, the algorithm of step 20 is run to update the class table. Preferably, the algorithm of step 20 runs as an infinite loop to continuously update the class table. Alternatively, it may be run only periodically or as new data becomes available.

Refine Class Identifiers (FIG. 3)

Once the class table is updated, the class identifiers are refined. Again, refining class identifiers includes discovering new class identifiers, dropping out unnecessary class identifiers, and modifying thresholds of current class identifiers. The means for refining the class identifiers is preferably a digital processor. FIG. 3 shows an algorithm, detailing step 22, for refining class identifiers. Here, ND represents the total number of conditions to be searched, and n represents a given condition.

The processor starts at step 58 and proceeds to read the class table at step 60. At step 62, the processor counts the classes and subsequently determines, at step 64, whether the total number of classes to be searched has been considered. If all the classes have been considered, the processor stops at step 64. If all the classes have not been considered, the processor continues to step 66 where it determines, from the class table, whether class n contains the minimum number of specimens needed to continue.

The optimal minimum number of specimens for refinement is approximately 250. However, for initial discovery of class identifiers, the minimum number of specimens can be much lower. Therefore, the minimum number of specimens needed for continuation preferably is about 45.

If at least the minimum number of specimens is placed in class n, the processor continues to step 68. At step 68, the processor refines the class identifiers for class n.

At step 70, the specificity and sensitivity (positive predictive value) of classification based on the refined class identifiers are calculated. If they are greater than about 70%, the class identifiers and thresholds are recorded at step 72. As a cross-check, a person then evaluates the refined algorithm before it is actually implemented to classify patients.

To search for new class identifiers, drop unnecessary class identifiers, and modify thresholds at step 68 of FIG. 3, specimens in the database are first categorized based on their condition. For simplification, two categories are considered here, NORMAL and DISEASED. However, there may be more categories depending on the class distinction. If a specimen's classification is unknown for the condition, then that specimen is excluded from the analysis.

The collection of potential class identifiers from the specimen profiles in the database is searched to determine class identifiers specific to a classification. Cluster analysis is the preferred method for searching, and any of a number of methods may be used to cluster specimens. Besides refining class identifiers (step 22), these methods can also be used to classify specimens (step 32). Some examples include:

Linear Methods

Linear Regression: Coefficients are calculated for all the class identifiers to get a linear equation, which would give vastly different values for specimens in different classes. A threshold value can be used as a separator to break the specimens into clusters.

Proximity: The distance between points representing each specimen is calculated in multi-dimensional Euclidian geometry, while seeking mutually exclusive clustering of the specimens. Removal of one or more of the dimensions, which represent potential class identifiers, is tried in an attempt to reduce the overlap and increase the distance between the clusters. The best case is chosen, and the potential class identifiers forming the dimensions of the space are declared class identifiers for the class.

Similarity/Dissimilarity: Instead of calculating the geometric distance between points representing specimens, each potential class identifier is studied one by one and required to be similar between the specimens. A similarity measure is calculated and minimized by removing the most offending potential class identifier from the set. The search is terminated when similarity is maximized/dissimilarity is minimized. The remaining potential class identifiers are declared class identifiers for the class.

Weighted Proximity: This method is similar to the Proximity analysis except that some potential class identifiers are weighted more heavily than others.

Principle component analysis: New variables are constructed as linear combinations of the entire set of potential class identifiers. Care is taken to make sure that these variables are orthogonal to each other. The total number of new variables should be much less than the number of potential class identifiers. Clustering is achieved using the methods listed above with the new variable set.

Non-Linear Methods:

Artificial Neural Networks: This is a layered and combinatorial processor where inputs are processed by artificial neurons. Neurons receive inputs as linear combinations of potential class identifiers, and output a value based on a non-linear transfer function, such as a sigmoidal function. There are usually three layers of neurons. The first, or input, layer and the second, or hidden, layer usually, but not necessarily, have the same number of neurons as the number of input potential class identifiers, and the third, or output, layer usually has one or two neurons.

Kohonen Networks: This method is very similar to artificial neural networks, except they are formed by a single layer with all neurons connected to each other. They are used for classification of input patterns formed by, in the present case, potential class identifiers.

Pattern Recognizers: Initially, a pattern for each class is formed. For example, average values for each potential class identifier can be calculated for each class. Potential class identifiers providing distinctions, i.e. small standard deviations with large differences in mean values, are kept as class identifiers. Specimens are classified by measuring their proximity or similarity to the mean values.

Empirical curve fitting: This method is similar to linear regression, but a non-linear equation is used instead of a linear equation. The non-linear equation can be a polynomial or can include, for example, exponential, logarithmic, and trigonometric functions. Potential class identifiers contributing maximally for the equation are declared the class identifiers.

Logical:

CART (Classification And Regression Trees): First, all potential class identifiers are individually scanned to identify one that results in the best classification. This yields two groups distinguishable by their values for the potential class identifier. Second, a scan of remaining potential class identifiers is performed for each of the groups to further classify specimens in each group. The process repeats until all or most of the specimens are classified correctly. Potential class identifiers selected for each stage are declared the class identifiers for the class.

Hierarchical Clustering: Multiple methods are possible, but the agglomerative method is described here as a representative example. Proximity or dissimilarity is calculated for each specimen. Specimens with maximum proximity or minimum dissimilarity are clustered and considered a new specimen, which replaces the specimens forming the cluster. The process is repeated until all specimens are clustered into two groups. If clustering is not optimal, then some of the potential class identifiers are removed and the process starts from scratch. Potential class identifiers used in the process that result in optimal clustering are declared the class identifiers.

Partitioning: This method is similar to hierarchical clustering and CART, but multiple thresholds for a given potential class identifier (pci) are used, which results in multiple pathways. For example,

-   IF pci7>threshold1 THEN . . . . -   IF threshold1≧pci7>threshold2 THEN . . . . -   IF threshold2≧pci7>threshold3 THEN . . . . -   IF threshold3≧pci7>THEN . . . .

New class identifiers are discovered, unnecessary class identifiers are dropped, and thresholds of known class identifiers are modified by optimizing the clustering of the specimens. For instance, clustering θ is preferred to clustering θ′, if f(θ)<f(θ′) where f(θ) is the function to be minimized. It can be defined as but is not limited to:

-   f(θ)=Radius_of_NORMAL_Cluster+Radius_of_DISEASED_Cluster−Distance_Between_Clusters,     for a given set of potential class identifiers θ     The optimal solution, -   θ is optimal, if f(θ)<f(θ′) for all solutions or, there is no     solution that is preferred to θ.

Diagnostic Process (FIG. 4)

FIG. 4 is a conceptual diagram showing diagnostic process 74, which identifies patients at high risk for “Heart Condition” that would benefit from an implantable medical device (IMD). Diagnostic process 74 may also be used, for example, to provide an overall risk assessment or risk stratification, which indicates whether a patient is more likely to suffer from certain diseases or conditions over other diseases or conditions. Examples of other settings where diagnostic process 74 may be used include a pathology setting at a hospital or veterinary clinic, a research setting, or in any situation where biological specimens are classified. Process 74 includes patient P, clinician C, sample 76, medical information 78, delivery step 80, processing lab 82, bioinformatic database 84, classification step 86, follow-up step 88, refining step 90, and updating step 92.

In operation, patient P provides biological sample 76. Sample 76 may be any one or more of a variety of biological samples, which include blood, saliva, bodily fluids, cheek swabs, tissue samples or biopsies, microorganisms, etc. Clinician C provides medical information 78, which relates to patient P. Medical information 78 may include electronic information such as medical device interrogation data. This type of data may come from an IMD, a blood glucose monitor, an ultrasound machine, a magnetic resonance imaging machine, a CT scanner, electronic home monitoring systems (e.g. CareLink™), an external ECG monitor, or electronic medical records of clinical centers and IMD interrogation databases (e.g. PaceArt™). Medical information 78 may also include physiological measurements such as ejection fraction, organ dimensions, blood pressure, renal output, data from external, trancutaeous, or indwelling sensors, etc. Other relevant information includes demographic information, medical history including psychiatric conditions, family history, reported symptoms, and past and current diagnoses and treatments (pathological).

At delivery step 80, sample 76 is brought to processing lab 82 where proteomic, genomic, and lipidomic information is extracted from sample 76 through any of a variety of ways. For example, protein analysis may include gel electrophoresis, mass spectrometry, Enzyme-Linked ImmunoSorbent Assay (ELISA), etc. Genetic analysis may include the detection of insertions and deletions, microsatellites (stretches of DNA that consist of tandem repeats of a simple sequence of nucleotides, e.g., AAT repeated 15 times in succession) detection, detection of major rearrangements in the genome, single nucleotide polymorphism (SNP) detection, haplotyping, sequence analysis, restriction fragment length polymorphism (RFLP) detection, randomly amplified polymorphic DNA (RAPD) detection, amplified fragment length polymorphism (AFLP) detection, etc. Lipid analysis may include nuclear magnetic resonance (NMR) spectroscopy, electrospray mass spectroscopy (EMS), colorimetry, fluorimetry, gas chromatography, high performance liquid chromatography (HPLC), etc.

After all the molecular data is extracted from sample 76, a digital processor preferably enters the molecular data as well as medical information 78 into bioinformatic database 84. Bioinformatic database 84 houses all of the gathered information and data regarding patient P.

At classification step 86, patient P's information and data are analyzed and an algorithm is used to classify patient P based on class identifiers. The class identifiers may also be referred to as disease identifiers or disease markers. That information is then made accessible to clinician C. Depending on the type of service that is provided and the reason for patient P's enrollment, patient P's classification may take many forms. Here, patient P is being classified as benefiting or not benefiting from an IMD. At this point, patient P's classification may or may not be stored in bioinformatic database 84. The means for adding the classification to database 84 is preferably a processor.

Once a course of action is implemented for patient P based on the classification, patient P is followed as represented by follow-up step 88. Each time patient P returns for a follow-up visit with clinician C, updated information and data is gathered. The means of gathering follow-up data and information can be any medically accepted technique. So for example, patient P is classified as being at risk for Heart Condition and further classified as benefiting from an IMD. At a follow-up visit, data gathered from the IMD, including the operational history of the IMD, indicated that the IMD was never triggered even though the data also showed that patient P would have benefited from it. Thus, patient P should not be included in the class of patients that benefit from an IMD.

At refining step 90, patient P's classification is corrected based on the follow-up data and entered in bioinformatic database 84. If patient P's initial classification was entered into database 84, then patient P is reclassified. The means for reclassifying patient P is preferably a processor. The updated bioinformatic database 84 is used to refine class identifiers as described previously.

Lastly, at updating step 92, any new class identifiers, dropped class identifiers, and modified thresholds are updated for implementing classification step 86. Updating step 92 is preferably carried out with an algorithm run on a processor. Thus, diagnostic process 74 improves itself not only by adding patient information and data to bioinformatic database 84, but also by correcting any incorrect classifications.

Specimen Profile

Table 1, shown below, is an example of the data storage format, or specimen/patient profile, entered into bioinformatic database 84 for each patient. The profile is not limited by the type of information and data that it contains. Any information and data gathered from a specimen may be contained in the profile. Here, it is simplified for ease of discussion and illustration. TABLE 1 PATENT ID COL- COLUMN 2 COLUMN 3 COLUMN 4 UMN 1 DATE 1 DATE 2 DATE 3 FIXED PHYSIOLOGICAL PHYSIOLOGICAL PHYSIOLOGICAL INFO DATA DATA DATA Sex WEIGHT WEIGHT WEIGHT Date of HEIGHT HEIGHT HEIGHT Birth Race Body Mass Index Body Mass Index Body Mass Index Ejection Fraction Ejection Fraction Ejection Fraction EGM EGM EGM Q-T interval Q-T interval Q-T interval SNP 1 Hemoglobin Count Hemoglobin Count Hemoglobin Count SNP 2 T-wave alternans T-wave alternans T-wave alternans SNP 3 Smoking Smoking Smoking SNP 4 CABG CABG CABG SNP 5 Stents Stents Stents SNP 6 Alcohol Alcohol Alcohol Consumption Consumption Consumption PATHOLOGICAL PATHOLOGICAL PATHOLOGICAL DATA DATA DATA Diabetes Diabetes Diabetes Myocardial Infarct. Myocardial Infarct. Myocardial Infarct. Arrhythmias Arrhythmias Arrhythmias NYHC NYHC NYHC Deafness Deafness Deafness Drugs Drugs Drugs MOLECULAR MOLECULAR MOLECULAR DATA DATA DATA ProteinSpectrum 1 ProteinSpectrum 1 ProteinSpectrum 1 ProteinSpectrum 2 ProteinSpectrum 2 ProteinSpectrum 2 ProteinSpectrum 3 ProteinSpectrum 3 ProteinSpectrum 3 Lipid Spectrum Lipid Spectrum Lipid Spectrum Protein 1 Protein 1 Protein 1 Protein 2 Protein 2 Protein 2 Protein 3 Protein 3 Protein 3 FAMILY FAMILY FAMILY HISTORY HISTORY HISTORY Column 1 contains fixed information that will not change over time and is gathered upon patient P's enrollment in diagnostic process 74. In Table 1, this includes patient P's sex, date of birth, race, and genetic information. The genetic information is also considered molecular data, but it will not change over time. However, new genetic information can be added over time as it is proven to be relevant. Examples listed in Table 1 include SNP 1-6.

Columns 2, 3, and 4 contain data that may change over time and, therefore, is periodically gathered from patient P. The data contained in these columns is divided into four categories: physiological data, pathological data, molecular data, and family history. Examples of physiological data are shown in Table 1 and include weight, height, body mass index, ejection fraction, EGM data, Q-T interval, hemoglobin count, T-wave alternans, smoking habits, number of coronary artery bypass grafts (CABG), stents, and alcohol consumption. Next, examples of pathological data are listed and include the presence of any diseases or conditions such as diabetes, myocardial infarction, arrhythmias, New York Heart Classification (NYHC), deafness, and drug regimens. Lastly, examples of listed molecular data include protein spectra 1-3, a lipid spectrum, and proteins 1-3. The lists in Table 1 are intended to be examples, and the specific data that is gathered and listed will depend on the purpose for the classification. For diagnostic process 74, EGM data is collected, which is stored in the memory of patient P's IMD. One way to obtain the IMD data is through telemetry from the IMD to a portable unit at the site of patient P. The data, in its entirety or in compressed form, can be subsequently transferred directly or indirectly to bioinformatic database 84 using any of a number of data transmission techniques. This data set might contain, but is not limited to, intracardiac electrogram data; pseudo-ECG data; histograms for heart rate, physical activity, minute volume, and pacing activity; intracardiac blood pressure data; transthoracic impedance (lung wetness) data; patient activation data; data on stimulation thresholds and trends; lead tip accelerometer data; and venous blood oxygen saturation data. The data is stored in its entirety and can be processed to extract further relevant data including, but not limited to, heart rate variability, QRS width, QT interval measurements, frequency of ventricular defibrillation shocks, duration and frequency of atrial fibrillation, atrial fibrillation burden, and correlation between syncopic episodes and heart rate drop. All of this data is added into patient P's profile.

Data generated in column 2 is gathered upon patient P's enrollment in the diagnostic service. Data generated in columns 3 and 4 is gathered with each subsequent follow-up visit to clinician C or whenever subsequent biological samples are obtained from patient P. Though only columns 3 and 4 are shown representing follow-up data of patient P, the number of columns of follow-up data is infinite. In addition, because a patient's medical information and data may be incomplete or contain contradictions, diagnostic process 74 is designed to compensate for these problems should they occur.

Classification Step (FIG. 5)

After the initial data is gathered upon patient P's enrollment and a profile generated, the profile is analyzed to classify patient P at classification step 86. FIG. 5 illustrates a representative, hypothetical diagnostic algorithm to carry out classification step 86, which classifies patient P as to his/her risk for Heart Condition. Classification is based on four class identifiers: I_(A), I_(B), I_(C), and I_(D). Class identifiers I_(A)-I_(D) may be derived from any of the medical information and data included in patient P's profile. For instance, class identifier I_(A) may be based on sex, I_(B) may be based on ejection fraction, and I_(C) and I_(D) may be based on levels of protein 1 and protein 2, respectively. The values or data provided by patient P's profile are compared to threshold values, T_(F/M), T_(B), T_(C), and T_(D), determined for each class identifier, respectively.

First, the sex of patient P is determined from the profile. If patient P is female (T_(F)), patient P is placed into group 94. If patient P is male (T_(M)), patient P is placed into group 96. Placement in either group 94 or group 96 requires further testing to determine whether or not patient P is at risk for Heart Condition, but the testing occurs along different paths.

Upon placement into group 94, the value of patient P's ejection fraction measurements is compared to T_(B). If the value is greater than T_(B), patient P is placed into group 98 and classified as not at risk for Heart Condition. If, however, the value is less than or equal to T_(B), patient P is placed into group 100 and is evaluated further.

Lastly, upon placement into group 100, the value of patient P's protein 1 level is compared to T_(C). If the value is less than T_(C), patient P is placed into group 102 and classified as not at risk for Heart Condition. If the value is greater than or equal to T_(C), patient P is placed into group 104 and classified as at risk for Heart Condition.

If patient P is initially placed into group 96 based on sex, then the value of patient P's protein 2 level is compared to T_(D). If the value is less than TD, patient P is placed into group 106 and classified as not at risk for Heart Condition. If the value is greater than or equal to T_(D), patient P is placed into group 108, and further evaluation is performed.

Lastly, upon placement into group 108, the value of patient P's protein 1 level is compared to T_(C). If the value is less than T_(C), patient P is placed into group 110 and classified as not at risk for Heart Condition. However, if the value is greater than or equal to T_(C), patient P is placed into group 112 and classified as at risk for Heart Condition.

The process outlined in FIG. 5 is one way of classifying—where each class identifier is evaluated individually, and the patient is placed in a group before continuing on to the next class identifier. Alternatively, classification may be performed as a logical test such as an IF-THEN test. IF-THEN logic is implemented as follows:

-   IF     -   ((P_(A)=T_(F)) AND (P_(B)≧T_(B)) AND (P_(C)≧T_(C))) OR         ((P_(A)=T_(M)) AND (P_(D)≧T_(D)) AND (P_(C)≧T_(C))) -   THEN     -   PATIENT P IS AT RISK FOR HEART CONDITION         where P_(X) represents information or values obtained from         patient P's profile. The sensitivity and specificity of         classification step 86 are preferably at least about 70%.

Thresholds T of class identifiers I are determined through clinical studies, experience, or the present invention, as previously described. Threshold T differs for each class identifier I. A patient may be classified as belonging to a specific class when the patient's values are above threshold T for some class identifiers I and below threshold T for other class identifiers I.

EXAMPLE 1

Classification process 10 may also be utilized to identify class identifiers of a given condition. A representative clinical study to discover class identifiers is described here. Patients with coronary artery disease and meeting specific inclusion criteria were enrolled in the study. The patients were divided into two groups. The test group consisted of sixteen patients with an IMD and one sustained episode of ventricular tachyarrhythmia/ventricular fibrillation (VT/VF) with a cycle length less than or equal to 400 ms. The control group consisted of 32 patients that had no IMD and no known history of VT/VF.

Upon enrollment, each patient filled out an extensive questionnaire including medical information. Three blood samples were drawn from each patient, of which one was used for serum preparation. The serum samples were analyzed for proteomic, genomic, and lipidomic data. The results of the proteomic and genomic analysis will be discussed here.

Currently, there are numerous ways to detect differential concentrations or levels of a protein from various biological specimens. 2-D PAGE separation, such as Fluorescence 2-D Difference Gel Electrophoresis (DIGE), may be utilized to detect differences in protein levels between biological samples. In DIGE, up to three protein extracts are labeled with different fluorescent dyes, then combined and separated by 2-D PAGE. Images of the gel are captured at excitation wavelengths of each of the fluorescent labels, and the images are merged. The differences, which indicate differences in relative protein levels, between the protein extracts are detected by image analysis software.

Isotope-coded affinity tagging (ICAT™) uses stable isotope labeling to quantitatively analyze paired protein extracts. The proteins are then separated and identified by liquid chromatography and mass spectrometry. The isotope tags covalently bind to cysteine residues within a protein. Two different protein samples are labeled with two different tags that are identical except that one exists in a light isotopic form and the other in a heavy isotopic form. When bound to the same protein from different extracts, a definite mass change is detected through mass spectrometry. The relative abundance of protein pairs are determined to find pairs whose level is significantly different between the protein extracts.

Surface-enhanced laser desorption ionization-time of flight (SELDI-TOF) utilizes stainless steel or aluminum-based supports, or chips, having chemical (e.g. hydrophilic, hydrophobic, pre-activated, normal-phase, immobilized metal affinity, and cationic or anionic) or biological (e.g. antibody, antigen binding fragments, nucleic acid, enzyme, or receptor) bait surfaces that are 1-2 mm in diameter. These bait surfaces allow differential capture of proteins based on the intrinsic properties of the proteins themselves. Solubilized specimen samples are directly applied to the surface. Proteins with affinities to the bait surfaces bind, and unbound and weakly bound proteins are removed. The bound proteins are laser desorbed and ionized for analysis by mass spectrometry. The mass of the proteins are calculated based upon time-of-flight. Patterns of protein masses are produced that result in protein spectrums unique to each sample. Proteins that differ between samples are subsequently identified.

Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) is similar to SELDI-TOF except that protein samples are embedded in a crystal of matrix molecules that absorb at the laser wavelength. The energy absorption ionizes the proteins, and the mass is calculated based on time-of-flight resulting in protein spectrums.

Other methods may be employed to determine the relative level of proteins. For instance, the invention may be carried out by assaying individual protein markers with an ELISA. Protein arrays are a proteomic tool that is similar to DNA microarray technology. Protein-based chips array proteins on a small surface, and protein levels in specimens are measured directly using fluorescence-based imaging. Proteins are arrayed either on flat solid surfaces or in capillary systems called microfluidic arrays. Several different proteins can be applied to the arrays, with most of the current ones relying on antibody-antigen interactions.

These methods are presented only as examples. Any type of protein analysis may be used with the present invention. For instance, the relative abundance of proteins can also be measured indirectly by analysis of mRNA expressed in cells. DNA microarrays work by exploiting the ability of a given mRNA to specifically bind to the DNA template from which it originated. By using arrays with many DNA samples, the expression levels of hundreds or thousands of genes can be determined in a single experiment.

In the representative clinical study, proteins in the serum were initially fractionated into four groups based on the isoelectric point of the protein. The proteins were spotted onto three surfaces of one or more biochips. One surface was a weak cation exchange surface. Another surface was a hydrophobic surface. The last surface was an immobilized metal affinity surface. A protein spectrum fingerprint was obtained using SELDI-TOF.

FIG. 6 shows sample protein spectra 114. Protein spectra 114 includes protein spectra 116 and 118. Protein spectrum 116 originates from a patient in the test group, and protein spectrum 118 originates from a patient in the control group. Differences between protein spectra 116 and 118 indicate differences in the level of expression of proteins between the two samples. This was indicative of all the protein spectra obtained from all patients in the study showing potential class identifiers that, in the present case, are protein markers. The differences in the protein markers between the two groups may form a basis for distinguishing patients that would benefit from an IMD versus patients that would not benefit from an IMD.

FIG. 7 shows tree analysis 120. Tree analysis 120 applies the above results that identified candidate class identifiers, P1, P2, P3, and P4, to classify patients based on a high propensity for fatal VT/VF.

The serum levels of protein P1 were determined for all patients, represented as group 122. Patients having levels greater than or equal to 1.0422237 (measured in arbitrary units) were classified as not at significant risk for VT and, therefore, not a candidate for an IMD and placed into group 124. Patients having P1 levels less than 1.0422237 were placed into group 126 and were further evaluated.

P2 serum levels were next tested for patients in group 126. Patients having levels less than 0.2306074 were not candidates for an IMD and placed into group 128. Patients having levels greater than or equal to 0.2306074 were placed into group 130 and evaluated further.

P3 serum levels were then tested for patients in group 130. Patients having levels greater than or equal to 0.0491938 were not candidates for an IMD and placed in group 132. Patients having levels less than 0.0491938 were placed into group 134 and evaluated further.

Lastly, P4 serum levels were tested for patients in group 134. Patients having levels greater than or equal to 0.27011 were classified as being candidates for an IMD and placed in group 136. Patients having levels less than 0.27011 were not candidates for an IMD and placed into group 138.

The arbitrary units may be normalized to an abundant protein, such as albumin. The invention also supports using other benchmarks, such as the total ion current in the mass spectrometer or calibration using a known concentration of an exogenous protein introduced into the sample, to measure protein levels.

The four markers, or class identifiers, correctly classified the 48 patients. Specifics of these proteins are shown in Table 2 below: TABLE 2 Protein Molecular Isoelectric Capture Number Weight (Da) pH (pI) Surface P1 10,146.5 9+ Weak cation exchange P2 15,006 9+ Weak cation exchange P3 166,582 5-7 Weak cation exchange P4 10,948 9+ Immobilized metal affinity The proteins in Table 2 are characterized by their molecular weight and isoelectric point. It is not necessary to the invention that the proteins, or any class identifier, be specifically identified further.

Tree analysis 120 of FIG. 7 was expressed as an IF-THEN test and applied to clinical data where two biological samples from patients were processed. The results are shown in Table 3 below: TABLE 3 VT/VF NORMAL TEST (+) 27 1 TEST (−) 5 63

-   -   Sensitivity: (27/(27+5))×100=84%     -   Specificity: (63/63+1))×100=98%     -   False Positives: (1/(1+27))×100=4%     -   False Negatives: (5/(5+63))×100=7%

The sensitivity and specificity of conventional classification techniques, such as signal averaged electrocardiogram, tend to be approximately 55% to 75%. Thus, this data demonstrates a significant improvement in sensitivity and specificity.

Next, Tree analysis 120 of FIG. 7 was utilized in an artificial neural network and applied to the same clinical data. The artificial neural network had four input nodes corresponding to proteins P1-P4. The network included four hidden nodes and one output node. The results are shown in Table 4 below: TABLE 4 VT/VF NORMAL TEST (+) 24 1 TEST (−) 8 63

-   -   Sensitivity: (24/(24+8))×100=75%     -   Specificity: (63/(63+1))×100=98%     -   False Positives: (1/(1+25))×100=4%     -   False Negatives: (8/(8+63))×100=11%         Patients whose classification resulted in false positives and         false negatives, as shown in Tables 3 and 4, are subsequently         reclassified to further refine the class identifiers.

Depending on the type of class identifier, measurements of mass, concentration, levels, or abundance may be less important than simply determining if the class identifier is present or not. In other cases, the level detected on a single occasion is not important. Instead, the threshold is based on an increase or decrease in the levels or the rate of change, as demonstrated by two or more measurements separated by a specific time interval.

Genomic analysis was also carried out using patients participating in the same study. In this case, SNP rs1009531 was identified as a class identifier. SNP rs1009531 is located on gene KCND3, which codes for voltage-gated potassium channel, Shal-related family, member 3 (a.k.a. Kv4.3). Patients that are homozygous thymine/thymine have a 75% chance of experiencing a fatal arrhythmia, while patients that are homozygous cytosine/cytosine have only a 30% chance of experiencing a fatal arrhythmia. On the other hand, patients that are heterozygous thymine/cytosine have a 50% chance of experiencing a fatal arrhythmia. In this case, the partitioning method provided the best clustering of the patients for risk stratification.

EXAMPLE 2

A second representative clinical study to discover class identifiers was carried out with an additional 30 patients. These additional patients also had coronary artery disease and met specific inclusion criteria. They were divided into the two groups based on whether or not patients had an IMD. The patients having an IMD also had at least one true VT/VF episode with a cycle length less than or equal to 400 ms terminated in the last 90 days. A total of 29 patients were in the test group, which consists of patients with an IMD, and 49 patients were in the control group.

Patients filled out an extensive questionnaire that included medical information that was then used in creating specimen profiles. Table 5 shows the patient characteristics included the specimen profile and the relative breakdown between the test and control groups. TABLE 5 Patients in ICD Patients in Total Arm Control Arm Patients Patient Characteristics (N = 29) (N = 49) (N = 78) Gender (N, %) Male 29 (100%) 49 (100%) 78 (100%) Female  0 (0%)  0 (0%)  0 (0%) Age (years) Mean 68.8 67.1 67.8 Standard Deviation  8.2  8.1  8.1 Minimum-Maximum 50-81 51-81 50-81 Left Ventricular Ejection Fraction Time since most recent LVEF (days) Mean  1.1  0.9  1 Standard Deviation  1.1  1.1  1.1 Minimum-Maximum  0-5.3  0-4.8  0-5.3 Most Recent Documented Measurement (%) Mean 37.9 51.2 46.2 Standard Deviation  9.6  9.5 11.5 Minimum-Maximum 28-66 29-73 28-73 Method of LVEF measurement Radionuclide  6 (21%) 19 (39%) 25 (32%) angiocardiography/MUGA Echo  9 (31%) 16 (33%) 25 (32%) Cath 13 (45%) 14 (29%) 27 (35%) Unknown  0 (0%)  0 (0%)  0 (0%)

Table 6 shows patient cardiovascular surgical and medical history that was included in the specimen profiles and the relative breakdown between the control and test groups. TABLE 6 Patients in Patients in Total ICD Arm Control Arm Patients Patient Characteristics (N = 29) (N = 49) (N = 78) Cardiovascular Surgical History (N, %) None  6 (20.7%)  4 (8.2%) 10 (12.8%) Coronary Artery Bypass Graft 14 (48.3%) 20 (40.8%) 34 (43.6%) Coronary Artery Intervention 13 (44.8%) 36 (73.5%) 49 (62.8%) Angioplasty  8 (27.6%) 25 (51%) 33 (42.3%) Stent  7 (24.1%) 31 (63.3%) 38 (48.7%) Atherectomy  0 (0%)  0 (0%)  0 (0%) Ablation  3 (10.3%)  3 (6.1%)  6 (7.7%) Valvular Surgery  1 (3.4%)  1 (2%)  2 (2.6%) Other  1 (3.4%)  2 (4.1%)  3 (3.8%) Cardiovascular Medical History None  0 (0%)  0 (0%)  0 (0%) Coronary Artery Disease 29 (100%) 49 (100%) 78 (100%) Myocardial Infarction 29 (100%) 49 (100%) 78 (100%) Number of infarctions Mean  1.4  1.3  1.3 Standard Deviation  0.6  0.5  0.5 Minimum-Maximum 1-3 1-3 1-3 Time Since First Infarction (years) Mean 10.2  6.6  7.9 Standard Deviation  7.7  5.6  6.6 Minimum-Maximum 1-26 0-22 0-26 Time Since Most Recent Infarction (years) Mean  5.3  5  5.1 Standard Deviation  5  5.4  5.2 Minimum-Maximum 1-17 0-22 0-22 Hypertension 19 (65.5%) 34 (69.4%) 53 (67.9%) Cardiomyopathy 18 (62.1%)  3 (6.1%) 21 (26.9%) Hypertrophic  5 (17.2%)  0 (0%)  5 (6.4%) Dilated  9 (31%)  3 (6.1%) 12 (15.4%) Valve Disease/Disorder  4 (13.8%)  8 (16.3%) 12 (15.4%) Aortic  0 (0%)  5 (10.2%)  5 (6.4%) Tricuspid  1 (3.4%)  1 (2%)  2 (2.6%) Mitral  4 (13.8%)  3 (6.1%)  7 (9%) Pulmonary  0 (0%)  0 (0%)  0 (0%) Primary/Idiopathic Electrical  0 (0%)  1 (2%)  1 (1.3%) Conduction Disease Documented Accessory  0 (0%)  0 (0%)  0 (0%) Pathway Chronotropic Incompetence  1 (3.4%)  0 (0%)  1 (1.3%) NYHA Classification Class I  4 (13.8%)  3 (6.1%)  7 (9%) Class II  6 (20.7%)  5 (10.2%) 11 (14.1%) Class III  4 (13.8%)  0 (0%)  4 (5.1%) Class IV  0 (0%)  0 (0%)  0 (0%) Not Classified 15 (51.7%) 41 (83.7%) 56 (71.8%) Congenital Heart Disease  0 (0%)  0 (0%)  0 (0%) Other  2 (6.9%)  3 (6.1%)  5 (6.4%)

Table 7 shows patient arrhythmia history that was included in the specimen profiles and the relative breakdown between the control and test groups. TABLE 7 Patients in Patients in Total ICD Arm Control Arm Patients Patient Characteristics (N = 29) (N = 49) (N = 78) Spontaneous Arrhythmia History (N, %) None  0 (0%) 26 (53.1%) 26 (33.3%) Ventricular Sustained Monomorphic VT 23 (79.3%)  0 (0%) 23 (29.5%) Sustained Polymorphic VT  1 (3.4%)  0 (0%)  1 (1.3%) Nonsustained VT 17 (58.6%)  0 (0%) 17 (21.8%) Ventricular Flutter  1 (3.4%)  0 (0%)  1 (1.3%) Ventricular Fibrillation  9 (31%)  0 (0%)  9 (11.5%) Torsades de Pointes  0 (0%)  0 (0%)  0 (0%) Long Q/T Syndrome  0 (0%)  0 (0%)  0 (0%) Other  0 (0%)  2 (4.1%)  2 (2.6%) Bradyarrythmias/Conduction Disturbances Sinus Bradycardia  5 (17.2%) 14 (28.6%) 19 (24.4%) Sick Sinus Syndrome  0 (0%)  2 (4.1%)  2 (2.6%) 1° AV Block  7 (24.1%)  6 (12.2%) 13 (16.7%) 2° AV Block  0 (0%)  0 (0%)  0 (0%) Type I (Mobitz)  0 (0%)  0 (0%)  0 (0%) Type II (Wenckebach)  0 (0%)  0 (0%)  0 (0%) 3° AV Block  0 (0%)  0 (0%)  0 (0%) Right Bundle Branch Block  5 (17.2%)  8 (16.3%) 13 (16.7%) Left Bundle Branch Block  4 (13.8%)  2 (4.1%)  6 (7.7%) Bradycardia-Tachycardia  0 (0%)  0 (0%)  0 (0%) Syndrome Other  2 (6.9%)  0 (0%)  2 (2.6%) Atrial Arrythmia History (N, %) None 14 (48.3%) 36 (73.5%) 50 (64.1%) Atrial Tachycardia  4 (13.8%)  0 (0%)  4 (5.1%) Paroxysmal  4 (13.8%)  0 (0%)  4 (5.1%) Recurrent  0 (0%)  0 (0%)  0 (0%) Chronic  0 (0%)  0 (0%)  0 (0%) Atrial Flutter  1 (3.4%)  5 (10.2%)  6 (7.7%) Paroxysmal  1 (3.4%)  4 (8.2%)  5 (6.4%) Recurrent  0 (0%)  1 (2%)  1 (1.3%) Chronic  0 (0%)  0 (0%)  0 (0%) Atrial Fibrillation 11 (37.9%)  9 (18.4%) 20 (25.6%) Paroxysmal  8 (27.6%)  3 (6.1%) 11 (14.1%) Recurrent  0 (0%)  4 (8.2%)  4 (5.1%) Chronic  3 (10.3%)  1 (2%)  4 (5.1%)

Table 8 shows patient family history that was included in the specimen profiles and the relative breakdown between the control and test groups. TABLE 8 Patients in Patients in Total ICD Arm Control Arm Patients Patient Characteristics (N = 29) (N = 49) (N = 78) Patient Family History (N, %) None 20 (69%) 31 (63.3%) 51 (65.4%) Long Q/T Syndrome  0 (0%)  0 (0%)  0 (0%) Grandparent  0 (0%)  0 (0%)  0 (0%) Parent  0 (0%)  0 (0%)  0 (0%) Sibling  0 (0%)  0 (0%)  0 (0%) Cousin  0 (0%)  0 (0%)  0 (0%) Sudden Cardiac Death  5 (17.2%) 11 (22.4%) 16 (20.5%) Grandparent  0 (0%)  1 (2%)  1 (1.3%) Parent  4 (13.8%)  8 (16.3%) 12 (15.4%) Sibling  1 (3.4%)  3 (6.1%)  4 (5.1%) Cousin  0 (0%)  1 (2%)  1 (1.3%) Sudden Death  3 (10.3%)  4 (8.2%)  7 (9%) Grandparent  0 (0%)  0 (0%)  0 (0%) Parent  3 (10.3%)  3 (6.1%)  6 (7.7%) Sibling  1 (3.4%)  1 (2%)  2 (2.6%) Cousin  0 (0%)  0 (0%)  0 (0%) Syncope  2 (6.9%)  1 (2%)  3 (3.8%) Grandparent  0 (0%)  0 (0%)  0 (0%) Parent  2 (6.9%)  0 (0%)  2 (2.6%) Sibling  0 (0%)  1 (2%)  1 (1.3%) Cousin  0 (0%)  0 (0%)  0 (0%) Deafness  1 (3.4%)  4 (8.2%)  5 (6.4%) Grandparent  0 (0%)  1 (2%)  1 (1.3%) Parent  1 (3.4%)  2 (4.1%)  3 (3.8%) Sibling  0 (0%)  1 (2%)  1 (1.3%) Cousin  0 (0%)  0 (0%)  0 (0%) History of Thrombo-embolic Event No 24 (82.8%) 44 (89.8%) 68 (87.2%) Yes  5 (17.2%)  5 (10.2%) 10 (12.8%) Time since most recent event (years) Mean  1.8  4.2  3.2 Standard Deviation  1.4  6.3  4.7 Minimum-Maximum 0.8-3.4 0.2-13.5 0.2-13.5 Type TIA  2 (6.9%)  1 (2%)  3 (3.8%) CVA  2 (6.9%)  1 (2%)  3 (3.8%) PE  0 (0%)  1 (2%)  1 (1.3%) Renal  0 (0%)  0 (0%)  0 (0%) Peripheral  0 (0%)  0 (0%)  0 (0%) Other  1 (3.4%)  2 (4.1%)  3 (3.8%) Other History History of Hyperthyroidism  0 (0%)  2 (4.1%)  2 (2.6%) Hearing loss 12 (41.4%) 16 (32.7%) 28 (35.9%)

Table 9 shows patient lifestyle characteristics that were included in the specimen profiles and the relative breakdown between the control and test groups. TABLE 9 Patients in ICD Patients in Total Arm Control Arm Patients Patient Characteristics (N = 29) (N = 49) (N = 78) Does the Patient Smoke? No 23 (79.3%) 42 (85.7%) 65 (83.3%) Yes  6 (20.7%)  7 (14.3%) 13 (16.7%) Number of Years Mean 41.6 39.6 40.4 Standard Deviation 11.1  8  9 Minimum-Maximum 30-55 30-50 30-55 Degree of Smoking 1-2 packs a week  1 (3.4%)  2 (4.1%)  3 (3.8%) 3-5 packs a week  0 (0%)  1 (2%)  1 (1.3%) 5-10 packs a week  2 (6.9%)  3 (6.1%)  5 (6.4%) 10 or more packs a week  1 (3.4%)  0 (0%)  1 (1.3%) Use of Alcohol No 17 (58.6%) 24 (49%) 41 (52.6%) Yes 12 (41.4%) 25 (51%) 37 (47.4%) Number of Years Mean 36.7 38.2 37.7 Standard Deviation 17 13.9 14.7 Minimum-Maximum 10-59  4-60  4-60 Degree of Drinking 1-2 drinks a week  5 (17.2%)  6 (12.2%) 11 (14.1%) 3-5 drinks a week  1 (3.4%)  7 (14.3%)  8 (10.3%) 5-10 drinks a week  4 (13.8%)  6 (12.2%) 10 (12.8%) 10 or more drinks a week  2 (6.9%)  5 (10.2%)  7 (9%)

Table 10 shows patient baseline medications that were included in the specimen profiles and the relative breakdown between the control and test groups. TABLE 10 Patients in ICD Patients in Total Arm Control Arm Patients Patient Medications (N = 29) (N = 49) (N = 78) Any Medications in Prior 6 Months (N, %) No  0 (0%)  0 (0%)  0 (0%) Yes 29 (100%) 49 (100%) 78 (100%) Class I  4 (13.8%)  1 (2%)  5 (6.4%) Disopyramide  0 (0%)  0 (0%)  0 (0%) Flecainide  0 (0%)  0 (0%)  0 (0%) Mexiletine  1 (3.4%)  0 (0%)  1 (1.3%) Moricizine  0 (0%)  0 (0%)  0 (0%) Procainamide  2 (6.9%)  0 (0%)  2 (2.6%) Propafenone  0 (0%)  1 (2%)  1 (1.3%) Quinidine  1 (3.4%)  0 (0%)  1 (1.3%) Tocainide  0 (0%)  0 (0%)  0 (0%) Other  0 (0%)  0 (0%)  0 (0%) Class III 14 (48.3%)  4 (8.2%) 18 (23.1%) Amiodarone  8 (27.6%)  2 (4.1%) 10 (12.8%) Dofetilide  0 (0%)  0 (0%)  0 (0%) Sotalol  7 (24.1%)  2 (4.1%)  9 (11.5%) Other  0 (0%)  0 (0%)  0 (0%) Beta Blockers 17 (58.6%) 36 (73.5%) 53 (67.9%) Atenolol  1 (3.4%)  9 (18.4%) 10 (12.8%) Betaxolol  0 (0%)  0 (0%)  0 (0%) Bisoprolol  0 (0%)  0 (0%)  0 (0%) Bucindolol  0 (0%)  0 (0%)  0 (0%) Carvedilol  4 (13.8%)  3 (6.1%)  7 (9%) Metoprolol 11 (37.9%) 22 (44.9%) 33 (42.3%) Nadolol  0 (0%)  0 (0%)  0 (0%) Penbutolol  0 (0%)  0 (0%)  0 (0%) Propranolol  1 (3.4%)  2 (4.1%)  3 (3.8%) Timolol  0 (0%)  0 (0%)  0 (0%) Other  0 (0%)  1 (2%)  1 (1.3%) Calcium Channel Blockers  4 (13.8%) 10 (20.4%) 14 (17.9%) Amlodipine  2 (6.9%)  4 (8.2%)  6 (7.7%) Diltiazem  0 (0%)  3 (6.1%)  3 (3.8%) Ibepridil  0 (0%)  0 (0%)  0 (0%) Felodipine  0 (0%)  0 (0%)  0 (0%) Nifedipine  0 (0%)  3 (6.1%)  3 (3.8%) Nisoldipine  0 (0%)  0 (0%)  0 (0%) Nimodipine  0 (0%)  0 (0%)  0 (0%) Verapamil  1 (3.4%)  0 (0%)  1 (1.3%) Other  1 (3.4%)  0 (0%)  1 (1.3%) Digoxin  9 (31%)  3 (6.1%) 12 (15.4%) Anti-Coagulants 28 (96.6%) 46 (93.9%) 74 (94.9%) Warfarin  5 (17.2%)  8 (16.3%) 13 (16.7%) Aspirin 25 (86.2%) 40 (81.6%) 65 (83.3%) Other  4 (13.8%) 14 (28.6%) 18 (23.1%) Other 26 (89.7%) 39 (79.6%) 65 (83.3%)

Protein analysis from patient blood samples was carried out as described in the previous example. The CART (Classification and Regression Tree) method was used to identify class identifiers. The iterative partitioning algorithm used the test versus control groupings as the response variables and 2076 predictor variables that included 86 demographic variables and 1990 protein/peptide variables. Eligible protein/peptide variables were identified as peaks in spectral analyses of at least 4% of patients. Each patient's protein level for a given variable was averaged from two peak measurements.

FIG. 8 is the resulting tree analysis from the CART analysis. Tree analysis 140 uses the five identified class identifiers, P5, P6, P7, P8, and P9, to classify all patients, represented as group 142, based on risk for fatal VT/VF.

The most effective class identifier is protein P5. Fifteen patients having P5 levels less than 0.0300685765 (measured in arbitrary units) were placed in group 144. Thirteen, or 86.7%, were test patients with IMDs. Sixty-three patients having P5 levels greater than or equal to 0.0300685765 were placed in group 146. Sixteen, or 25.4%, were test patients.

The class identifier shown to further partition group 144 and represented as P6 was consumption of alcohol or lack of consumption for less than 20 years. Ten patients (eight of which had never consumed alcohol) were placed in group 148. All 10 patients, or 100%, were test patients. Five patients that had consumed alcohol for more than 20 years were placed in group 150. Three of these five patients, or 60%, were test patients.

Group 146 was then further partitioned based on levels of protein P7. Twenty patients having P7 levels less than or equal to 0.1759674485 were placed in group 152. All 20, or 100%, were control patients. Forty-three patients having P7 levels greater than 0.1759674485 were placed in group 154. Twenty-seven of these 43 patients, or 62.8%, were control patients.

Group 154 was further partitioned based on levels of protein P8. Thirteen patients having P8 levels less than 0.314539267 were placed in group 156. All 13, or 100%, were control patients. Thirty patients having P8 levels greater than or equal to 0.314539267 were placed in group 158. Fourteen of these 30 patients, or 46.7%, were control patients.

Further partitioning of group 158 was based on levels of protein P9. Thirteen patients having P9 levels less than 0.0935425805 were placed into group 160. Twelve of these patients, or 92.3%, were test patients. Seventeen patients having P9 levels greater than or equal to 0.0935425805 were placed into group 162. Only four of these 17 patients, or 23.5%, were test patients.

Thus, when applying tree analysis 140, patients falling into groups 148 and 160 have a significant risk of experiencing VT/VF and would benefit from an IMD. Conversely, patients falling into groups 152, 156, and 162 do not have a significant risk of experiencing VT/VF.

Table 11 summarizes the percentage of test patients belonging to each group of tree analysis 140. TABLE 11 P5 P6 P7 P8 P9 Col. 6 Col. 7* <0.0300685765 Subtotal 15 86.7 ≧20 years 5 60 <20 years 10 100 ≧0.0300685765 N/A Subtotal 63 25.4 ≧0.1759674485 20 0 <0.1759674485 Subtotal 43 37.2 <0.314539267 13 0 ≧0.314539267 Subtotal 30 53.3 ≧0.0935425805 17 23.5 <0.0935425805 13 92.3 Total 78 37.2 *Each percentage is for the applicable group of the corresponding row; therefore, the percentages do not sum to 100%, as they are calculated with different denominators (patient sample sizes). Columns one through five represent the class identifiers, P5-P9 and rows represent groups 144-162 obtained by using the class identifiers. Column 6 (Col. 6) is the total number of patients belonging to each corresponding group, and column 7 (Col. 7) is percentage of patients in each group that are test patients.

For example, to assess the percentage of test patients among all patients having P5 levels greater than or equal to 0.0300685765 and P7 levels less than 0.1759674485, begin at column 1 and select the row corresponding to ≧0.0300685765. Move to columns 2 and 3 (column 2 does not apply to these patients) and select the row in column 3 corresponding to <0.1759674485. Moving across to column 6, the number of patients having these class identifiers is 43, and the corresponding row in column 7 indicates that 37.2% of the 43 patients were test patients.

Table 12 is a summary of information regarding proteins P5, P7, P8, and P9. TABLE 12 Partitioning Molecular Fraction of Spectrum Peak Weight Chip Type Isolation Range Intensity P5 Immobilized Combined High Protein 0.0300685765 11991 Metal Affinity fractions f2 Surface and f3 containing pH 5-7 P7 Weak Cation Fraction 1 High Protein 0.1759674485 10552.4 Exchange containing Surface flow-through and pH = 9 P8 Weak Cation Fraction 1 High Protein 0.314539267 43529.4 Exchange containing Surface flow-through and pH = 9 P9 Hydrophobic Fraction 1 Protein 0.0935425805 13806.8 Surface containing flow-through and pH = 9

This analysis resulted in four protein class identifiers and one demographic class identifier that correctly classifies patients based on risk of experiencing a true VT/VF episode.

A genetic analysis of the same group of patients identified additional class identifiers useful in predicting a patient's risk of experiencing VT/VF. DNA from each patient was extracted from cells in the blood samples. There were 162 gene SNPs (Single Nucleotide Polymorphisms) for which genotypes were obtained for the patients. In the discussion regarding genetics, the following abbreviations are used: a=adenine, t=thymine, c=cytosine, and g=guanine.

A chi-square test was performed using the response variable (test, control) and each of the 162 SNP variables. Table 13 lists SNPs and resulting p-values identified as corresponding to genotypes and patient groups that were not independent. TABLE 13 Gene SNP # of Genotypes P-value rs2239507 3 0.0051 rs7626962 2 0.0094* rs3743496 3 0.012* rs723672 6 0.0060* rs2072715 2 0.0473* rs12276475 3 0.0092* rs1544503 3 0.0349* rs3752158 3 0.0465* rs1023214 3 0.0322* rs730818 3 0.0372 rs1842082 3 0.0434 rs7578438 5 0.0443* rs545118 3 0.0415* rs802351 5 0.0314* *Fisher's exact test used, as criteria for chi-square test not met

In some cases the expected cell counts were too small, and it was determined that the chi-square test was not reliable. Instead, Fisher's exact test was used. Fourteen SNPs produced p-values less than 0.05.

SNP rs2239507 is located at position chr12:5021395, Build 123, within the KCNA5 gene (Accession No. NT_(—)009759). The position information provides the chromosome and nucleotide position, and the Build references the sequence update information from which the SNP position was obtained. The most updated information is given, however, as genome information is generated, the position may change slightly. KCNA5 codes for the potassium voltage-gated channel, subfamily A, member 5 protein. This protein mediates the voltage-dependent potassium ion permeability of excitable membranes. rs2239507 and its flanking regions were amplified using the following primers: 5′-agt cag gat cag gta ttt ttc ct-3′ (SEQ ID NO. 1) and 5′-aga acc cag gtg aac caa t-3′ (SEQ ID NO. 2). The SNP site was sequenced using the following oligo: 5′-aga tag agt cga tgc cag ctt cat ggg tct ctg acc tca ctg tct-3′ (SEQ ID NO. 3). Table 14 includes the statistical breakdown of allele distribution within the patient groups. TABLE 14 Patient SNP rs2239507 Group g/g g/t t/t Total Test (N, %) 12 7 10 29 41.4% 24.1% 34.5% 100% Control 5 21 23 49 (N, %) 10.2% 42.9% 46.9% 100% Total (N, %) 17 28 33 78 21.8% 35.9% 42.3% 100% As shown in Table 14, twelve, or 41.4%, of test patients had the genotype g/g, whereas only five, or 10.2%, of control patients had the genotype g/g.

rs7626962 is located at position chr3:38595911, Build 116, within the SCN5A gene (Accession No. NT_(—)022517). The SCN5A gene codes for the sodium channel, voltage-gated, type V, alpha (long QT syndrome 3) protein. This protein produces sodium channels that transport sodium ions across cell membranes and plays a key role in generating and transmitting electrical signals. rs7626962 and its flanking regions were amplified using the following primers: 5′-cct ccg gat tcc agg acc-3′ (SEQ ID NO. 4) and 5′-ttc cgc ttt cca ctg ctg-3′ (SEQ ID NO. 5). The SNP site was sequenced using the following oligo: 5′-gga tgg cgt tcc gtc cta tta gat gca ctg gcc tcg gcc tca gag-3′ (SEQ ID NO. 6). Table 15 shows the statistical breakdown of the patient groups. TABLE 15 Patient SNP rs7626962 Group g/g t/t Total Test (N, %) 23 6 29 79.3% 20.7% 100% Control 48 1 49 (N, %) 98.0% 2.0% 100% Total (N, %) 71 7 78 91.0% 9.0% 100%

rs3743496 is located at position chr15:71401461, Build 121, within the gene HCN4 (Accession No. NT_(—)010194). The gene codes for the potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4 protein. The protein is a hyperpolarization-activated ion channel that may contribute to native pacemaker currents in the heart. rs3743496 and its flanking regions were amplified using the following primers: 5′-att gtg ttc att tag aga aac agc t-3′ (SEQ ID NO. 7) and 5′-ctt ctg agg ctc ccc agg-3′ (SEQ ID NO. 8). The SNP site was sequenced using the following oligo: 5′-agg gtc tct acg ctg acg atc tgt aac ttg gag ctc cac tct gcc-3′ (SEQ ID NO. 9). Table 16 shows the statistical breakdown of the patient groups. “Frequency Missing” indicates that the SNP could not be read for one patient sample. One or more patient samples could not be read for other SNPs, and this is indicated in the tables below. TABLE 16 Patient SNP rs3743496 Group a/a c/a c/c Total Test (N, %) 2 3 24 29 6.9% 10.3% 82.8% 100% Control 0 0 48 48 (N, %) 0.0% 0.0%  100% 100% Total (N, %) 2 3 72 77 2.6% 3.9% 93.5% 100% Frequency Missing = 1

rs2072715 is located at position chr22:38339084, Build 121, within the gene CACNA1I (Accession No. NT_(—)011520). The gene codes for the calcium channel, voltage-dependent, alpha 1I subunit protein. The protein is a voltage-sensitive calcium channel that serves pacemaking functions in central neurons and cardiac nodal cells. rs2072715 and its flanking regions were amplified using the following primers: 5′-tca gta gga aat gaa ggc ttt t-3′ (SEQ ID NO. 10) and 5′-att tca ggg aac gaa tgg a-3′ (SEQ ID NO. 11). The SNP site was sequenced using the following oligo: 5′-gcg gta ggt tcc cga cat att ctt caa gca gcg gga ggg ggt ggc-3′ (SEQ ID NO. 12). Table 17 includes the statistical breakdown of the allelic distribution within the patient groups. TABLE 17 Patient SNP rs2072715 Group g/a g/g Total Test (N, %) 8 21 29 27.6% 72.4% 100% Control 4 45 49 (N, %) 8.2% 91.8% 100% Total (N, %) 12 66 78 15.4% 84.6% 100%

rs12276475 is located at position chr11:29989733, Build 120, within the KCNA4 gene (Accession No. NT_(—)009237). This gene codes for the potassium voltage-gated channel, shaker-related subfamily, member 4 protein. This protein mediates the voltage-dependent potassium ion permeability of excitable membranes. rs12276475 and its flanking regions were amplified using the following primers: 5′-aca aac tca aag gaa aac cat aca-3′ (SEQ ID NO. 13) and 5′-atc tcg tca tgg cac tga gt-3′ (SEQ ID NO. 14). The SNP site was sequenced using the following oligo: 5′-gtg att ctg tac gtg tcg cct ggg ttg ttg aat gat act tca gca-3′ (SEQ ID NO. 15). Table 18 includes the statistical breakdown for the patient groups. TABLE 18 Patient SNP rs12276475 Group a/a c/a c/c Total Test (N, %) 11 18 0 29 37.9% 62.1% 0.0% 100% Control 33 15 1 49 (N, %) 67.3% 30.6% 2.1% 100% Total (N, %) 44 33 1 78 56.4% 42.3% 1.3% 100%

rs1544503 is located at position chr12:2308902, Build 123, within the CACNA1C gene (Accession No. NT_(—)009759). The protein is involved in mediating entry of calcium ions into excitable cells and plays an important role in excitation-contraction coupling in the heart. rs1544503 and its flanking regions were amplified using the following primers: 5′-aat agg atg cac ttg ctt gac-3′ (SEQ ID NO. 16) and 5′-atg agg aag agt ccc ttc acc-3′ (SEQ ID NO. 17). The SNP site was sequenced using the following oligo: 5′-agg gtc tct acg ctg acg att gat gtt cat tga tgg gga cag gca-3′ (SEQ ID NO. 18). Table 19 shows the statistical breakdown. TABLE 19 Patient SNP rs1544503 Group c/c t/c t/t Total Test (N, %) 16 7 5 28 57.1% 25.0% 17.9% 100% Control 21 24 2 47 (N, %) 44.7% 51.1% 4.3% 100% Total (N, %) 37 31 7 75 49.3% 41.3% 9.3% 100% Frequency Missing = 3

rs723672 is located at position chr12:2031822, Build 120, within the CACNA1C gene (Accession No. NT_(—)009759). The function of the gene product was discussed previously. rs723672 and its flanking regions were amplified using the following primers: 5′-tat ctg tca ctt cta caa ccg ct-3′ (SEQ ID NO. 19) and 5′-aat tcc aag gag gag gaa tac a-3′ (SEQ ID NO. 20). The SNP site was sequenced using the following oligo: 5′-gcg gta ggt tcc cga cat ata tcg ggc cac tga aca aaa cgg caa-3′ (SEQ ID NO. 21). Table 20 shows the statistical breakdown. TABLE 20 Patient SNP rs723672 Group a/a g/a g/g Total Test 2 15 11 28 (N, %) 7.1% 53.6% 39.3% 100% Control 13 15 19 47 (N, %) 27.7% 31.9% 40.4% 100% Total 15 30 30 75 (N, %) 20.0% 40.0% 40.0% 100% Frequency Missing = 3

rs3752158 is located at position chr19:558984, Build 120, within the HCN2 gene (Accession No. NT_(—)011255). This gene codes for the hyperpolarization activated cyclic nucleotide-gated potassium channel 2 protein. This protein may play a role in generation of neuronal pacemaker activity. rs3752158 and its flanking regions were amplified using the following primers: 5′-atg cac agc atg tgg ctc-3′ (SEQ ID NO. 22) and 5′-tga gca cct gcc cac cac-3′ (SEQ ID NO. 23). The SNP site was sequenced using the following oligo: 5′-agg gtc tct acg ctg acg att gca gaa cca ctc gtg gag tga act-3′ (SEQ ID NO. 24). Table 21 shows a statistical breakdown of the allelic distribution. TABLE 21 Patient SNP rs3752158 Group c/c c/g g/g Total Test (N, %) 5 2 20 27 18.5% 7.4% 74.1% 100% Control 1 7 40 48 (N, %) 2.1% 14.6% 83.3% 100% Total (N, %) 6 9 60 75 8.0% 12.0% 80.0% 100% Frequency Missing = 3

rs1023214 is located at position chr1:234128106, Build 123, within the RYR2 gene (Accession No. NT_(—)004836). This gene codes for the ryanodine receptor 2 protein. The protein is involved in providing calcium required for cardiac muscle excitation-contraction coupling. rs1023214 and its flanking regions were amplified using the following primers: 5′-agt aaa gca tta tgg agg cat aaa-3′ (SEQ ID NO. 25) and 5′-gca tct aag ttc tcc taa att ttt tat t-3′ (SEQ ID NO. 26). The SNP site was sequenced using the following oligo: 5′-ggc tat gat tcg caa tgc ttg ttt gga tta tga cat cat tct ata-3′ (SEQ ID NO. 27). Table 22 shows a statistical breakdown of the allelic distribution. TABLE 22 Patient SNP rs1023214 Group a/a g/a g/g Total Test (N, %) 16  3 3 22 72.7% 13.6% 13.6% 100% Control 15 15 2 32 (N, %) 46.9% 46.9%  6.3% 100% Total (N, %) 31 18 5 54 57.4% 33.3%  9.3% 100% Frequency Missing = 24

rs730818 is located at position chr17:40227887, Build 86, within the GJA7 gene (Accession No. NT_(—)010783). The gene codes for the gap junction alpha-7 (Connexin 45) protein. This protein is involved in cell-to-cell communication. rs730818 and its flanking regions were amplified using the following primers: 5′-ttt ttc ttt cag aag ccc ct-3′ (SEQ ID NO. 28) and 5′-aca tga cat ggt gac aag ca-3′ (SEQ ID NO. 29). The SNP site was sequenced using the following oligo: 5′-cgt gcc gct cgt gat aga att ctt tgt caa ttg act ttt tct ccc-3′ (SEQ ID NO. 30). Table 23 includes a statistical breakdown of the allelic distribution. TABLE 23 Patient SNP rs730818 Group a/a g/a g/g Total Test (N, %)  0 16  9 25  0.0% 64.0% 36.0% 100% Control 10 21 18 49 (N, %) 20.4% 42.9% 36.7% 100% Total (N, %) 10 37 27 74 13.5% 50.0% 36.5% 100% Frequency Missing = 4

rs1842082 is located at position chr1:233951526, Build 123, within the RYR2 gene (Accession No. NT_(—)004836). This gene codes for the gap junction alpha-7 protein as described above. rs1842082 and its flanking regions were amplified using the following primers: 5′-aag aaa aaa tac aaa gac agt ggc-3′ (SEQ ID NO. 31) and 5′-tgt caa aac ctt tgg ttc aaa-3′ (SEQ ID NO. 32). The SNP site was sequenced using the following oligo: 5′-agg gtc tct acg ctg acg att tgt taa gcc tcc ttc ccg tta ttc-3′ (SEQ ID NO. 33). Table 24 is a statistical breakdown of the patient groups. TABLE 24 Patient SNP rs1842082 Group c/c c/g g/g Total Test (N, %)  5 19  5 29 17.2% 65.5% 17.2% 100% Control 21 19  9 49 (N, %) 42.9% 38.8% 18.4% 100% Total (N, %) 26 38 14 78 33.3% 48.7% 18.0% 100%

rs545118 is located at position chr11:30002105, Build 123, within the KCNA4 gene (Accession No. NT_(—)009237). The gene product was described previously. rs545118 and its flanking regions were amplified using the following primers: 5′-cat ttt tac aga caa gaa aat tta gg-3′ (SEQ ID NO. 34) and 5′-ata ggt ttt tct ctc cag cc-3′ (SEQ ID NO. 35). The SNP site was sequenced using the following oligo: 5′-acg cac gtc cac ggt gat ttc ttg gct gca gaa cct ctg gct aag-3′ (SEQ ID NO. 36). Table 25 shows the statistical breakdown of the patient groups. TABLE 25 Patient SNP rs545118 Group a/a t/a t/t Total Test (N, %) 19 10 0 29 65.5% 34.5%  0.0% 100% Control 20 24 5 49 (N, %) 40.8% 49.0% 10.2% 100% Total (N, %) 39 34 5 78 50.0% 43.6%  6.4% 100%

rs7578438 is located at position chr2:155388192, Build 121, within the KCNJ3 gene (Accession No. NT_(—)005403). This gene codes for the potassium inwardly-rectifying channel, subfamily J, member 3 protein. This protein forms potassium channels that are characterized by a greater tendency to allow potassium to flow into the cell rather than out of it. rs7578438 and its flanking regions were amplified using the following primers: 5′-cat aaa tct taa ctt tta gcg atc g-3′ (SEQ ID NO. 37) and 5′-agg atc gtt ttc tag ata caa atg tat aa-3′ (SEQ ID NO. 38). The SNP site was sequenced using the following oligo: 5′-agc gat ctg cga gac cgt atg ata tgg tct aga tca aca ata att-3′ (SEQ ID NO. 39). Table 26 includes a statistical breakdown of the patient groups. TABLE 26 Patient SNP rs7578438 Group g/g g/t t/t Total Test  9 16 2 27 (N, %) 33.3% 59.3%  7.4% 100% Control 14 29 6 49 (N, %) 28.6% 59.2% 12.2% 100% Total 23 45 8 76 (N, %) 30.3% 59.2% 10.5% 100% Frequency Missing = 2

rs802351 is located at position chr7:119166671, Build 123, within the KCND2 gene (Accession No. NT_(—)007933). This gene codes for the potassium voltage-gated channel, subfamily D, member 2 protein. This protein is a pore-forming subunit of voltage-gated, rapidly inactivating A-type potassium channels. rs802351 and its flanking regions were amplified using the following primers: 5′-aaa act ata agt att ttc ttg tga agg tg-3′ (SEQ ID NO. 40) and 5′-aac att tgc caa tgc aat g-3′ (SEQ ID NO. 41). The SNP site was sequenced using the following oligo: 5′-gga tgg cgt tcc gtc cta tta gca gca ttt aaa taa atg ccc tct-3′ (SEQ ID NO. 42). Table 27 includes a statistical breakdown of the allelic distribution. TABLE 27 Patient SNP rs802351 Group g/g g/t t/t Total Test 1 10 17 28 (N, %) 3.6% 35.7% 60.7% 100% Control 2  9 38 49 (N, %) 4.1% 18.4% 77.6% 100% Total 3 19 55 77 (N, %) 3.9% 24.7% 71.4% 100% Frequency Missing = 1

Using the same data, a second analysis was performed to compare major and minor allele frequencies among the patient groups. The major allele is that which occurs most frequently, while the minor allele is that which occurs least frequently. Each patient has two alleles, therefore, 156 alleles are assessed for each SNP. It was determined whether patients with particular alleles in their genotype were more likely to belong to one group over the other.

Table 28 shows the results of either a chi-square or Fisher's exact test analysis where at least one of the alleles resulted in a p-value less than 0.05. TABLE 28 Gene/Accession No. SNP Major Allele P-value Minor Allele P-value Refer to discussion rs12276475 a 0.4388 C (test) 0.0113 KCNK4 rs1320840 g 0.6372 a (control) 0.0293 NT_033903 KCNA4 rs1323860 a (test) 0.022 g 0.4671 NT_009237 KCND3 rs1538389 c (control) 0.0448* t 0.3341 NT_019273 KCND3 rs1808973 t 0.6998 c (control) 0.0443 NT_019273 Refer to discussion rs1842082 c 0.9003 g (test) 0.0204 KCND2 rs1859534 t (test) 0.7039 a 0.0317 NT_007933 Refer to discussion rs2072715 g N/A** a (test) 0.0473* Refer to discussion rs2238043 g (test) 0.0462* a 0.142 Refer to discussion rs2239507 t (control) 0.0013 g 0.2819 SLC8A1 rs2373860 t (control) 0.0419 a 0.7046 NT_022184 KCNK4 rs3739081 a (control) 0.0368 g 0.7621 NT_033903 Refer to discussion rs3743496 c 0.0653 a (test) 0.0060* Refer to discussion rs3752158 g (control) 0.0207* c 0.3359 KCND2 rs3814463 t (control) 0.050* c 0.1629 NT_007933 KCNQ1 rs4930127 g (control) 0.0309* a 0.2581 NT_009237 Refer to discussion rs545118 a 0.0754 t (control) 0.035 Refer to discussion rs723672 g 0.6552 t (test) 0.0168* KCND3 rs730022 c (test) 0.0227* t 0.2121 NT_019273 Refer to discussion rs730818 g (test) 0.0134* a 0.9505 Refer to discussion rs7578438 g 0.1077 a (test) 0.0137* Refer to discussion rs7626962 g (control) 0.0094* t (test) 0.0094* CACNA1C rs765125 t (test) 0.0335 c 0.9409 NT_009759 Refer to discussion rs802351 t (control) 0.0310* c (test) 0.0151* *Fisher's exact test used as criteria for chi-square test not met. **All patients had at least one allele g, so no test was performed. Column 1 indicates the gene and Accession Number for SNPs not previously discussed. Columns 3 and 5 indicate the patient group in parentheses, which had a higher percentage of patients with the allele in question if there was a significant difference. For example, a higher percentage of test patients than control patients had the c allele in the gene SNP rs12276475.

Table 29 lists the chromosome position and Build number for each SNP. Table 29 additionally includes the primers used to amplify each SNP and the oligo used to sequence each SNP. SNPs described elsewhere are indicated. TABLE 29 SNP Position/ Gene SNP Build No. Primers Oligo rs12276475 Refer to discussion rs1320840 Chr2: 5′-aaa atg ttc agc ttg (SEQ ID NO. 43) 5′-agg gte tct acg ctg (SEQ ID NO. 45) 26860830 taa ttc ca-3′ acg atc ccc cca aag cta 111 5′-tag aag cta gag agg (SEQ ID NO. 44) gtg ttt agc tct-3′ aaa gtg aca a-3′ rs1323860 Chr11: 5′-aag agc cat gtg ggc (SEQ ID NO. 46) 5′-aga gcg agt gac gca (SEQ ID NO. 48) 29991812 cat-3′ tac tat ttc agc tca gtt 121 5′-aat att ttc aag agt (SEQ ID NO. 47) ttt tat ggt-3′ atg ggg ca-3′ rs1538389 Chr1: 5′-ata att ggg agc tgg (SEQ ID NO. 49) 5′-cgt gcc gct cgt gat (SEQ ID NO. 51) 112039186 agt agc t-3′ aga ata tct tag taa taa 88 5′-tca gag gac aca gta (SEQ ID NO. 50) tcc caa gca aac-3′ tct aag gc-3′ rs1808973 Chr1: 5′-ttg tgc tgg tgt acc (SEQ ID NO. 52) 5′-cgt gcc gct cgt gat (SEQ ID NO. 54) 112199876 tcg a-3′ aga atg gca gat gat ttg 123 5′-aga agg agt aaa ggc (SEQ ID NO. 53) ttg agc aga atg-3′ agc c-3′ rs1842082 Refer to the discussion above. rs1859534 Chr7: 5′-agt caa gtc cag tcc (SEQ ID NO. 55) 5′-agg gtc tct acg ctg (SEQ ID NO. 57) 119321505 aca gta ata ta-3′ acg ata tgt gtg ttt gtg 123 5′-aaa ata ctt aag gat (SEQ ID NO. 56) ctt ttt aga tta-3′ ata ctc taa agg ca-3′ rs2072715 Refer to the discussion rs2238043 Refer to the discussion rs2239507 Refer to the discussion rs2373860 Chr2: 5′-att tca act taa gta (SEQ ID NO. 58) 5′-agc gat ctg cga gac (SEQ ID NO. 60) 40547682 ttg aat cca aag-3′ cgt att ttt tct atg gtt 123 5′-gtt ttt ttc tct tat (SEQ ID NO. 59) ctt atg gct ata-3′ ctt tct ttt gtt c-3′ rs3739081 Chr2: 5′-ata aag aaa gga ggg (SEQ ID NO. 61) 5′-cga ctg tag gtg cgt (SEQ ID NO. 63) 26867272 caa gtg t-3′ aac tca aac agc taa atg 123 5′-tcc cac ctg cct ctg (SEQ ID NO. 62) caa caa tag cag-3′ tct-3′ rs3743496 Refer to the discussion rs3752158 Refer to the discussion. rs3814463 Chr7: 5′-aat tgg ttg tct tct (SEQ ID NO. 64) 5′-agc gat ctg cga gac (SEQ ID NO. 66) 118937980 ggg g-3′ cgt atg act gga agg caa 120 5′-att ttt tgg caa gtt (SEQ ID NO. 65) gac ccg caa agc-3′ gga ca-3′ rs4930127 Chr11: 5′-ctg tgc aga cgc cta (SEQ ID NO. 67) 5′-agc gat ctg cga gac (SEQ ID NO. 69) 2550553 agg-3′ cgt atg tga acc gcg ctg 120 5′-tgg gct ata ttg aag (SEQ ID NO. 68) gag cgg cgt agg-3′ ccg-3′ rs545118 Refer to the discussion rs723672 Refer to the discussion rs730022 Chr1: 5′-gat aac caa gag tta (SEQ ID NO. 70) 5′-acg cac gtc cac ggt (SEQ ID NO. 72) 112227635 acc ata att aca g-3′ gat tta agt aaa aat aaa 123 5′-taa gta tgc gtg tcc (SEQ ID NO. 71) cta atg ata ctc-3′ agg aa-3′ rs730818 Refer to the discussion rs7578438 Refer to the discussion rs7626962 Refer to the discussion rs765125 Chr12: 5′-tac tgt ggg caa cat (SEQ ID NO. 73) 5′-ggc tat gat tcg caa (SEQ ID NO. 75) 2026468 ttc ag-3′ tgc ttt tgg ggg att tcc 121 5′-atn ctg cac cct ctt (SEQ ID NO. 74) ccc aaa gcc ttc-3′ cag-3′ rs802351 Refer to the discussion

In nine of 15 cases where there was a significant difference between the patient groups in terms of the major allele, the control group had the higher percentage of patients with the allele. In eight of the eleven SNPs in which there was a significant difference between the patient groups in terms of minor allele frequency, the test group had the higher percentage of patients with the minor allele.

Next, the CART method was used to analyze the relationship between patient genotypes and the likelihood of experiencing a VT/VF episode. Test versus control group was the response variable, and the set of 86 demographic and 162 SNPs were the predictor variables. The resulting tree analysis 164 is shown in FIG. 9.

Tree analysis 164 begins by partitioning all patients, represented by group 142, based on the History of Stent variable. Thirty-eight patients had a stent and were placed into group 166, while 40 patients that had no stent and were placed into group 168. Of the patients in group 166, only seven, or 18.4% belonged to the test group. Conversely, of the patients in group 168, 22, or 55%, belonged to the test group. The p-value of this clustering using a chi-square test is 0.0007.

Group 166 was further partitioned by SNP rs1861064. Ten patients having the genotype c/g were placed in group 170, while 28 patients having genotypes g/g or c/c were placed in group 172.

Of the patients in group 170, five, or 50%, were test patients. Group 170 was further partitioned based on Patient Height. Five patients from group 170 were greater than or equal to 71 inches and placed into group 174. Four, or 80%, of these patients were test patients and are at risk of VT/VF. The remaining five patients from group 170 were less than 71 inches and placed into group 176. Four, or 80%, of these patients were control patients and, therefore, not at risk of VT/VF.

Patients in group 172 were further partitioned based on Patient Weight. Twenty-three patients from group 172 weighed greater than or equal to 165 lbs. and placed into group 178. All 26 patients were from the control group, and therefore, patients in this group are deemed not to be at significant risk of experiencing VT/VF.

The remaining five patients weigh less than 165 lbs. and were placed into group 180. Three patients, or 60%, were control patients, and no further assessment could be made for these patients.

Patients in group 168 were further partitioned based on SNP rs730818. Six patients having the genotype a/a were placed into group 182. All six patients were from the control group. Therefore, patients in group 182 are not at significant risk of VT/VF.

Thirty-four patients having the genotype g/a or g/g were placed in group 184. Group 184 was further partitioned based on SNP rs1852598. Eleven patients from group 184 having the genotype c/c or t/c were placed in group 186. All 11 patients were from the test group. Therefore, patients in group 184 are at significant risk of VT/VF.

The remaining 23 patients from group 184 having the genotype t/t were placed into group 188. Patients in group 188 were further partitioned based on SNP rs268779. Six patients having genotype g/g were placed into group 190. All six patients were from the control group, and therefore, patients in group 190 are not at significant risk of VT/VF.

The remaining 17 patients from group 188 were placed in group 192 and further partitioned based on SNP rs3739081. Seven patients having the genotype g/g were placed into group 194. All seven patients were from the test group and, therefore, deemed to be at significant risk of VT/VF.

Ten patients from group 192 had the genotype a/a or g/a and were placed in group 196. Patients in group 196 were further partitioned based on SNP rs909910. Five patients having genotype a/a or g/a were placed into group 198. All five patients belonged to the control group. Therefore, patients in group 198 are not at significant risk of experiencing fatal VT/VF.

The remaining five patients from group 196 had the genotype g/g and were placed into group 200. Four of the five patients were from the test group. Therefore, patients placed in group 200 are at risk of experiencing fatal VT/VF.

The three analyses indicate that patients who have recently experienced at least one episode of VT/VF differ from those who have not in at least one gene SNP. These SNPs, in turn, can be used as class identifiers for classifying patients. Table 30 summarizes information regarding the SNPs utilized in tree analysis 164. TABLE 30 SNP Position/ SNP/Gene Build No. Primers Oligo rs1861064 Chr7: 5′-tgt tgt gta tag ctt (SEQ ID NO. 76) 5′-gcg gat ggt tcc cga (SEQ ID NO. 78) KCND2 119132866 att atg aaa ctg a-3′ cat att ata aaa ttt gca 120 5′-tta ggc aaa aat gct (SEQ ID NO. 77) gtt tgt tta ctc-3′ acc aat c-3′ rs268779 Chr1: 5′-agt ggg ctc aac ttt (SEQ ID NO. 79) 5′-gga tgg cgt tcc gtc (SEQ ID NO. 81) RYR2 233736784 tac tgt t-3′ cta tta atc ttt aat aag 121 5′-gaa aga ttc ctg tca (SEQ ID NO. 80) aga ggc agt tac-3′ ctt g-3′ rs909910 Chr16 5′-agc agg tga gtg tcc (SEQ ID NO. 82) 5′-acg cac gtc cac ggt (SEQ ID NO. 102) CACNA1H 1138939 ttt g-3′ gat ttg cca ccc agt cag 86 5′-tgg atc cca aaa ttc (SEQ ID NO. 83) cag gta ttt att-3′ ctt g-3′ rs730818 Refer to the discussion above. rs1852598 Refer to the discussion above. rs3739081 Refer to the discussion above.

EXAMPLE 3

A third genetic study examining 451 SNPs was performed with an additional 12 patients for a total of 90 patient samples included in the study. Here, the control group consists of 52 patients, and the test group consists of 38 patients. The analysis was carried out as previously described, but the results are used to stratify patient risk of experiencing fatal VT/VF. This stratification scheme can be subsequently used as a class identifier in a classification algorithm.

rs1008832 is located at position chr12:2483782, Build 120, within the CACNA1C gene (Accession No. NT_(—)009759), which codes for the calcium channel, voltage-dependent, L type, alpha 1C subunit protein. The function of this protein was previously described. The SNP and its flanking regions were amplified using primers 5′-tgc aca tga aca aag ccc-3′ (SEQ ID NO. 84) and 5′-aaa gtt agg aaa gaa gaa gca gaa t-3′ (SEQ ID NO. 85). The SNP was sequenced using the oligo 5′-aga gcg agt gac gca tac taa ggc agg cag cag gtg tga gca gat-3′ (SEQ ID NO. 86). Table 31 shows the statistical breakdown of the genotypes for this SNP. TABLE 31 Count Total (%) Column (%) Row (%) Control Test Total t/t 28 11 39 31.1% 12.2% 43.3% 53.9% 29.0% 71.8% 28.2% t/c 19 20 39 21.1% 22.2% 43.3% 36.5% 52.6% 48.7% 51.3% c/c  5  7 12  5.6%  7.8% 13.3%  9.6% 18.4% 41.7% 58.3% Total 52 38 90 57.8% 42.2% The first (top) value in each cell is the number, or count, of patients placed in that set. The second value is the percentage of the total number of patients placed in the set. The third value is the percentage of patients from either the control or test group (depending on the column) packed in the set. The fourth value is the percentage of control or test patients (depending on the column) having a specific genotype from the total number of patients having that specific genotype. The bottom right cell is the total number of patients utilized for the SNP analysis. Here, 90 patients were used, however, in subsequent analyses the total number of patients may be less if a patient's sequence could not be read for a particular SNP. The information in Table 31 was subsequently used to calculate probabilities useful in stratifying patients as to their risk of VT/VF.

FIG. 10 is a mosaic plot illustrating the resulting risk stratification. The horizontal axis of the graph lists the possible genotypes at this particular SNP. The vertical axis is the probability of experiencing fatal VT/VF.

As shown in the graph, the presence of t at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of c at the SNP position. Specifically, patients with genotype t/t have almost a 75% probability of experiencing fatal VT/VF, while the t/c genotype indicates about a 50% probability, and a c/c genotype indicates about a 40% probability of experiencing fatal VT/VF.

rs2238043 is located at position chr12:2145924, Build 123, within the CACNA1C gene. The gene product was described above. The SNP and its flanking regions were amplified using primers 5′-ata cta gac aga gag caa gac ttc aag-3′ (SEQ ID NO. 87) and 5′-tcc cca ttc aaa gtg cct-3′ (SEQ ID NO. 88). The SNP was sequenced using the oligo5′-aga tag agt cga tgc cag ctg aag tga gat acc taa gga gtg tca-3′ (SEQ ID NO. 89). Table 32 shows the statistical breakdown of the genotypes for this SNP. TABLE 32 Count Total (%) Column (%) Row (%) CONTROL ICD Total g/g 13 16 29 14.4% 17.8% 32.2% 25.0% 42.1% 44.8% 55.2% g/a 29 21 50 32.2% 23.3% 55.6% 55.8% 55.3% 58.0% 42.0% a/a 10  1 11 11.1%  1.1% 12.2% 19.2%  2.6% 90.9%  9.1% Total 52 38 90 57.8% 42.2%

The information in Table 32 was used to calculate probabilities of patient VT/VF. FIG. 11 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of a at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of g at the SNP position. Patients with genotype a/a have about a 90% probability of experiencing fatal VT/VF, while the g/a genotype indicates about a 60% probability, and the g/g genotype indicates about a 45% probability of experiencing fatal VT/VF.

rs198544 was previously described. Table 33 shows the statistical breakdown of the genotypes for this SNP. TABLE 33 Count Total (%) Column (%) Row (%) CONTROL ICD Total c/c 17  5 22 18.9%  5.6% 24.4% 32.7% 13.2% 77.3% 22.7% c/g 27 23 50 30.0% 25.6% 55.6% 51.9% 60.5% 54.0% 46.0% g/g  8 10 18  8.9% 11.1% 20.0% 15.4% 26.3% 44.4% 55.6% Total 52 38 90 57.8% 42.2% The information in Table 33 was used to calculate probabilities of patient VT/VF. FIG. 12 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of c at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of g at the SNP position. Patients with genotype c/c have just over a 75% probability of experiencing fatal VT/VF, while the c/g genotype indicates about a 50% probability, and the g/g genotype indicates about a 45% probability of experiencing fatal VT/VF.

rs1009531 was previously described. Table 34 shows the statistical breakdown of the genotypes for this SNP. TABLE 34 Count Total (%) Column (%) Row (%) CONTROL ICD Total t/t  2  4  6  2.3%  4.6%  6.8%  3.9% 10.8% 33.3% 66.7% t/c 21 20 41 23.9% 22.7% 46.6% 41.2% 54.1% 51.2% 48.8% c/c 28 13 41 31.8% 14.8% 46.6% 54.9% 35.1% 68.3% 31.7% Total 51 37 88 58.0% 42.1% The information in Table 34 was used to calculate probabilities of patient VT/VF. FIG. 13 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of c at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of t at the SNP position. Patients with genotype c/c have about a 70% probability of experiencing fatal VT/VF, while the t/c genotype indicates about a 50% probability, and the t/t genotype indicates about a 35% probability of experiencing fatal VT/VF.

rs2121081 is located at position chr2:155530837, Build 123, within the KCNJ3 gene (Accession No. NT_(—)05403), which codes for the potassium inwardly-rectifying channel, subfamily J, member 3 protein. The protein plays a role in regulating the heartbeat. The SNP and its flanking regions were amplified using primers 5′-agg tga tga aag aaa tga acc ttt-3′ (SEQ ID NO. 90) and 5′-tag agc tgg gat gcg gcc-3′ (SEQ ID NO. 91). The SNP was sequenced using the oligo 5′-aga tag agt cga tgc cag ctg tcg tct gac acc aca gta ctt act-3′ (SEQ ID NO. 92). Table 35 shows the statistical breakdown of the genotypes for this SNP. TABLE 35 Count Total (%) Column (%) Row (%) CONTROL ICD Total c/c 14 15 29 15.6% 16.7% 32.2% 26.9% 39.5% 48.3% 51.7% c/g 24 20 44 26.7% 22.2% 48.9% 46.2% 52.6% 54.6% 45.5% g/g 14 3 17 15.6% 3.3% 18.9% 26.9% 7.9% 82.4% 17.7% Total 52 38 90 57.8% 42.2%

The information in Table 35 was used to calculate probabilities of patient VT/VF. FIG. 14 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of g at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of c at the SNP position. Patients with genotype g/g have about an 85% probability of experiencing fatal VT/VF, while the c/g genotype indicates about a 55% probability, and the c/c genotype indicates just under a 50% probability of experiencing fatal VT/VF.

rs1428568 was previously described. Table 36 shows the statistical breakdown of the genotypes for this SNP. TABLE 36 Count Total (%) Column (%) Row (%) CONTROL ICD Total t/t 9 12 21 10.5% 14.0% 24.4% 18.8% 31.6% 42.9% 57.1% t/a 22 17 39 25.6% 19.8% 45.4% 45.8% 44.7% 56.4% 43.6% a/a 17 9 26 19.8% 10.5% 30.2% 35.4% 23.7% 65.4% 34.6% Total 48 38 86 55.8% 44.2% The information in Table 36 was used to calculate probabilities of patient VT/VF. FIG. 15 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of a at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of t at the SNP position. Patients with genotype a/a have about a 65% probability of experiencing fatal VT/VF, while the t/a genotype indicates about a 55% probability, and the t/t genotype indicates about a 40% probability of experiencing fatal VT/VF.

rs918050 was previously described. Table 37 shows the statistical breakdown of the genotypes for this SNP. TABLE 37 Count Total (%) Column (%) Row (%) CONTROL ICD Total c/c 6 8 14 7.2% 9.6% 16.9% 12.5% 22.9% 42.9% 57.1% c/t 16 13 29 19.3% 15.7% 34.9% 33.3% 37.1% 55.2% 44.8% t/t 26 14 40 31.3% 16.9% 48.2% 54.2% 40.0% 65.0% 35.0% Total 48 35 83 57.8% 42.2% The information in Table 37 was used to calculate probabilities of patient VT/VF. FIG. 16 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of t at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of c at the SNP position. Patients with genotype t/t have about a 65% probability of experiencing fatal VT/VF, while the c/t genotype indicates about a 55% probability, and the c/c genotype indicates about a 40% probability of experiencing fatal VT/VF.

rs1483312 is located at position chr5:45550841, Build 123, within the HCN1 gene (Accession No. NT_(—)006576), which codes for the hyperpolarization activated cyclic nucleotide-gated potassium channel 1 protein. The protein contributes spontaneous rhythmic activity in the heart. The SNP and its flanking regions were amplified using primers 5′-tat cct aaa aat cct gct tta att tg-3′ (SEQ ID NO. 93) and 5′-tac atc tag ttg tat agt tct tat ctc taa att atc-3′ (SEQ ID NO. 94). The SNP was sequenced using the oligo 5′-ggc tat gat tcg caa tgc ttg aaa gca tat tac caa taa aaa tta-3′ (SEQ ID NO. 95). Table 36 shows the statistical breakdown of the genotypes for this SNP. TABLE 36 Count Total (%) Column (%) Row (%) CONTROL ICD Total t/t 2 6 8 2.3% 6.7% 9.0% 3.9% 16.2% 25.0% 75.0% t/a 16 16 32 18.0% 18.0% 36.0% 30.8% 43.2% 50.0% 50.0% a/a 34 15 49 38.2% 16.9% 55.1% 65.4% 40.5% 69.4% 30.6% Total 52 37 89 58.4% 41.6% The information in Table 36 was used to calculate probabilities of patient VT/VF. FIG. 17 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of a at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of t at the SNP position. Patients with genotype a/a have about a 70% probability of experiencing fatal VT/VF, while the t/a genotype indicates about a 50% probability, and the t/t genotype indicates about a 25% probability of experiencing fatal VT/VF.

rs1859037 is located at position chr7:90526702, Build 120, within the AKAP9 gene (Accession No. NT_(—)007933), which codes for the A-kinase (PRKA) anchor protein (yotiao) 9. The protein binds to the regulatory subunit of protein kinase A and confines the holoenzyme to discrete locations in a cell. The SNP and its flanking regions were amplified using primers 5′-aat taa tga ttg gta tga caa gtt atg a-3′ (SEQ ID NO. 96) and 5′-tga aag tta gat ttg tgt taa ctt cta tta g-3′ (SEQ ID NO. 97). The SNP was sequenced using the oligo 5′-gac ctg ggt gtc gat acc taa tag gtg cca taa gga aga gtc aga-3′ (SEQ ID NO. 98). Table 37 shows the statistical breakdown of the genotypes for this SNP. TABLE 37 Count Total (%) Column (%) Row (%) CONTROL ICD Total g/g 22 11 33 24.7% 12.4% 37.1% 43.1% 29.0% 66.7% 33.3% g/a 23 20 43 25.8% 22.5% 48.3% 45.1% 52.6% 53.5% 46.5% a/a 6 7 13 6.7% 7.9% 14.6% 11.8% 18.4% 46.2% 53.9% Total 51 38 89 57.3% 42.7% The information in Table 37 was used to calculate probabilities of patient VT/VF. FIG. 18 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of g at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of a at the SNP position. Patients with genotype g/g have about a 65% probability of experiencing fatal VT/VF, while the g/a genotype indicates about a 50% probability, and the ala genotype indicates about a 45% probability of experiencing fatal VT/VF.

rs6964587 is located at position chr7:90588196, Build 123, within the AKAP9 gene (Accession No. NT_(—)007933), which codes for the A-kinase (PRKA) anchor protein (yotiao) 9. This function of this protein was described above. The SNP and its flanking regions were amplified using primers 5′-gtg caa atg aaa caa gaa tta ata ag-3′ (SEQ ID NO. 99) and 5′-aat atg acc tta aag cat tct cca-3′ (SEQ ID NO. 100). The SNP was sequenced using the oligo 5′-cgt gcc gct cgt gat aga ata aca cat ggc aca gat gga gga aat-3′ (SEQ ID NO. 101). Table 38 shows the statistical breakdown of the genotypes for this SNP. TABLE 38 Count Total (%) Column (%) Row (%) CONTROL ICD Total g/g 5 7 12 5.7% 8.0% 13.6% 10.0% 18.4% 41.7% 58.3% g/t 23 20 43 26.1% 22.7% 48.9% 46.0% 52.6% 53.5% 46.5% t/t 22 11 33 25.0% 12.5% 37.5% 44.0% 29.0% 66.7% 33.3% Total 50 38 88 56.8% 43.2& The information in Table 38 was used to calculate probabilities of patient VT/VF. FIG. 19 is a mosaic plot illustrating the resulting risk stratification.

As shown in the plot, the presence of t at the SNP position indicates increased susceptibility to fatal VT/VF as compared to the presence of g at the SNP position. Patients with genotype t/t have about a 65% probability of experiencing fatal VT/VF, while the g/t genotype indicates about a 55% probability, and the g/g genotype indicates about a 40% probability of experiencing fatal VT/VF.

Each of these SNP tests can be used individually as class identifiers, or two or more SNP tests may be combined to improve the predictive power.

FIG. 20 is a contour plot showing the probability of experiencing VT/VF as a function of the allele specific inheritance pattern of SNPs rs2238043 and rs1483312. The horizontal axis is the possible genotypes of rs1483312, and the vertical axis is the possible genotypes of rs2238043. Matrixes were formed where the points of intersection are the points of interest. The box next to the plot identifies the probabilities that correspond to the intersection points.

The genotype combinations further stratify the patient probabilities to classify patients. For example, a patient having a genotype profile of t/t-g/g has a greater than 75% probability of experiencing VT/VF, while a patient having a genotype profile of t/a-g/a has a less than 75% probability. A patient having a genotype profile of a/a-g/a has a less than 50% probability of experiencing VT/VF, while a patient having a genotype profile of a/a-a/a has a less than 12.5% probability.

Target Identification

The present invention may also be used to identify targets for therapy as shown by step 40 of Classification process 10 (FIG. 1). Class identifiers identified at step 22 are studied to determine whether they may be targets for therapy.

If a newly discovered class identifier for a given disease is a protein marker, for example, there is a possibility that the protein marker plays a pivotal role in the disease pathway. Depending on whether the protein level is too high or too low in the disease state, protein levels may be increased or decreased to compensate for the atypical levels. This may be accomplished directly or indirectly through gene therapy to upregulate or downregulate protein expression or by injecting more of the protein to increase levels. Other factors, such as enzymes or antibodies, can be injected or induced to effectively decrease the protein levels.

The protein can also be studied to identify pharmaceutical drugs that act on the protein and resolve the disease or condition. In addition, even if it is determined that the protein is not directly involved in the condition's pathway, the protein can be studied to determine other factors that interact with it, which may lead to effective therapeutic targets.

Genomic data may identify a mutation, polymorphism, deletion, or repetition in a specific gene as a class identifier for a given disease or condition. Therapy can be applied at the genotypic level, such as through gene therapy. It may also be delivered at a phenotypic level, such as through the delivery of drugs.

Lipidomic data may identify atypical levels of or alterations in an insoluble blood serum factor. Again, this may be a target for drug therapy or gene therapy. If it is determined that the class identifier is not involved in the condition's pathway, studying the insoluble blood serum factor may lead to other factors that are effective targets.

Analysis of physiological data may discover a class identifier based on the electrical data gathered from the patient, such as a particular heart arrhythmia. An IMD may be utilized as a therapy for the particular condition.

A number of genes were identified and described above which may be involved in disease conditions that cause VT/VF. These may directly or indirectly be targets for therapy. In addition, other SNPs were identified whose genes were not listed, but which could also be used to find targets for therapy or study.

Improve Treatment Therapies

The present invention can also be used to improve treatment therapies. Class identifiers may be discovered that are based on specimens receiving a specific treatment versus not receiving the treatment. These class identifiers can be studied to provide insight into improving the treatment.

For example, recent evidence shows that VT/VF is treatable by administration of clonidine or vagal nerve stimulation, as well as through stimulation by an implantable cardioverter defibrillator (ICD). Thus, class identifiers may be used to identify patients that would benefit from these treatments and/or benefit from IMDs such as a drug pump to deliver intrathecal clonidine, a vagal nerve stimulator, or an ICD.

In this embodiment, information and data from specimens are gathered from samples prior to the specimen receiving a treatment. The specimens are classified based on being a candidate for therapy and class identifiers are identified.

Next, the specimens receive treatment. Follow-up information and data from specimens are gathered following treatment. The follow-up data also includes the classification of the specimen as to whether or not the specimen responded favorably to the treatment. The classification of the specimens is corrected based on the follow-up data.

Class identifiers are identified based on differences between specimens that responded favorably and specimens that did not respond favorably. Those class identifiers are studied further as potential targets for improving the treatment.

Some of the processes described above may be embodied as a computer-readable medium comprising instructions for a programmable processor. The programmable processor may include one or more individual processors, which may act independently or in concert. A “computer-readable medium” includes but is not limited to read-only memory, Flash memory, and a magnetic or optical storage medium. Furthermore, data can be transmitted over the Internet, local area networks, or wireless or land base telephone lines.

The present invention should reduce the number of false positives and false negatives. The invention is also self-improving in two ways. The addition of specimen profiles to the database and correctly classifying specimens or patients if initially incorrectly classified refine the algorithms to improve classification process 10.

Although the present invention has been described with reference to preferred embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention. 

1. A method of classifying a specimen, the method comprising: developing an algorithm for classifying specimens based on at least one class identifier; generating a specimen profile from the specimen; classifying, based on the specimen profile, the specimen into a class, the classifying being carried out using the algorithm; determining if reclassification of the specimen is necessary based on follow-up data; reclassifying the specimen if necessary; and refining the algorithm based on the reclassification.
 2. The method of claim 1 wherein the specimen profile includes data selected from a group consisting of demographic, historical, psychiatric, physiological, pathological, metabolic, genomic, proteomic, and lipidomic information.
 3. The method of claim 2 wherein the data includes electrical information.
 4. The method of claim 2 wherein the data includes electrical information collected from an implantable medical device.
 5. The method of claim 1 wherein the class identifier is based on a class distinction of previously classified specimens.
 6. The method of claim 1 wherein the specimen is an animal.
 7. The method of claim 1 wherein the specimen is a person.
 8. The method of claim 1 wherein the specimen is selected from a group consisting of a plant, tissue sample, bodily fluid, cellular sample, and subcellular sample.
 9. The method of claim 1 wherein the specimen is a microorganism.
 10. The method of claim 1 wherein refining the algorithm comprises: modifying threshold levels to increase sensitivity and specificity of classification.
 11. The method of claim 1 wherein refining the algorithm comprises: identifying new class identifiers.
 12. The method of claim 1 wherein refining the algorithm comprises: dropping class identifiers.
 13. The method of claim 1 wherein the algorithm is based on a plurality of class identifiers.
 14. A method of classifying a patient, the method comprising: generating a class table using information and data compiled in a database; deriving class identifiers based on the class table; classifying the patient using the class identifiers; determining if reclassification of the patient is necessary; updating the database to reflect reclassification of the patient; updating the class table using the updated database; and refining the class identifiers based on the updated class table.
 15. The method of claim 14 wherein refining the class identifiers further comprises: modifying threshold levels of the class identifiers.
 16. The method of claim 14 wherein refining the class identifiers further comprises: identifying new class identifiers.
 17. The method of class 14 wherein refining the class identifiers further comprises: dropping class identifiers.
 18. The method of claim 14 wherein the class table comprises a vector.
 19. The method of claim 14 wherein the information and data includes changes in levels of class identifiers over time.
 20. The method of claim 14 wherein the patient is classified by a first algorithm that is based on the class identifiers.
 21. The method of claim 20 wherein the first algorithm is a type selected from a group consisting of linear, non-linear, and logical algorithms.
 22. The method of claim 14 wherein the database and updated database includes at least one of demographic, historical, psychiatric, physiological, pathological, metabolic, genomic, proteomic, and lipidomic data and information.
 23. The method of claim 22 wherein the physiological data and information includes electrical data.
 24. The method of claim 23 wherein the electrical data is collected from an implantable medical device.
 25. A method of classifying a patient, the method comprising: identifying class identifiers based in part on electrical data; developing an algorithm for classifying patients based on the class identifiers; classifying the patient by analyzing patient data, including patient electrical data, with the algorithm; determining if reclassification of the patient is necessary based on follow-up data; and refining the algorithm based on the determination of the patient.
 26. The method of claim 25 wherein refining the algorithm comprises: identifying new class identifiers.
 27. The method of claim 25 wherein refining the algorithm comprises: modifying thresholds of the class identifiers.
 28. The method of claim 25 wherein refining the algorithm comprises: dropping class identifiers.
 29. The method of claim 25 wherein the patient electrical data is gathered from sensors selected from a group consisting of external sensors, transcutaneous sensors, and indwelling sensors.
 30. The method of claim 25 wherein the patient electrical data is gathered from a blood glucose monitor.
 31. The method of claim 25 wherein the electrical data and patient electrical data includes data selected from a group consisting of intracardiac electrogram data, pseudo-ECG data, heart rate data, physical activity data, minute volume data, pacing activity data, intracardiac blood pressure data, transthoracic impedance data, medical device activation data, stimulation thresholds and trends data, lead tip accelerometer data, and venous blood oxygen saturation data.
 32. The method of claim 25 and further comprising: processing the electrical data and patient electrical data to extract additional data.
 33. The method of 25 and further comprising: processing the electrical data and patient electrical data to extract data selected from a group consisting of heart rate variability data, QRS width data, QT interval measurements data, T-wave alternans data, frequency of ventricular defibrillation shocks data, duration and frequency of atrial fibrillation data, atrial fibrillation burden data, and correlation between syncopic episodes and heart rate drop data.
 34. A system of classifying specimens, the system comprising: a database containing specimen profiles that are clustered into classes based on class identifiers; means for classifying specimens using a first algorithm based on the class identifiers, and placing the specimens in classes based on the class identifiers; and means for improving the first algorithm based on follow-up data regarding accuracy of classification of the specimens.
 35. The system of claim 34 wherein the means for improving the first algorithm includes: means for deriving a class table using a second algorithm; and means for modifying thresholds of the class identifiers, identifying new class identifiers, and dropping class identifiers based upon analysis of the class table.
 36. A method of classifying patients, the method comprising: creating a database; identifying class identifiers; classifying patients using the class identifiers and an algorithm; providing follow-up data on the patients; reclassifying the patients based on the follow-up data; and updating the algorithm to reflect reclassification.
 37. The method of 36 wherein the follow-up data is gathered from the patients subsequent to classification.
 38. The method of 36 wherein the algorithm is of a type selected from a group consisting of linear, non-linear, and logical algorithms.
 39. A method of identifying patients who are candidates for a medical device, the method comprising: creating a database; gathering data from patients; analyzing the data using an algorithm, the algorithm identifying patients who are candidates for treatment with a medical device; adding the data to the database; correcting identification of the patients based upon follow-up data indicating a response of the patients to the medical device; and updating the database and algorithm using corrected identification of the patients.
 40. The method of 39 wherein the follow-up data further comprises: an operational history of the medical device.
 41. The method of claim 39 wherein the data and follow-up data include one or more of a group consisting of intracardiac electrogram data, pseudo-ECG data, heart rate data, physical activity data, minute volume data, pacing activity data, intracardiac blood pressure data, transthoracic impedance data, patient activation of the medical device data, stimulation threshold and trends data, lead tip accelerometer data, and venous blood oxygen saturation data.
 42. The method of claim 39 and further comprising: processing the data and the follow-up data to extract additional information.
 43. The method of claim 42 wherein the additional information includes heart rate variability, QRS width, QT interval measurements, T-wave alternans, frequency of ventricular defibrillation shocks, duration and frequency of atrial fibrillation, atrial fibrillation burden, and correlation between syncopic episodes and heart rate drop data.
 44. The method of claim 39 wherein the medical device is an implantable medical device.
 45. The method of claim 39 wherein the data and follow-up data are gathered from the medical device via telemetry.
 46. A method of identifying patients with disease, the method comprising: creating a database; deriving a disease table from the database; identifying disease markers based on the disease table; gathering medical information and biological data from the patients; identifying patients with disease based on the disease markers; adding the medical information, biological data, and disease identification of the patients to the database; determining, subsequent to disease identification, if re-identification of the patients is necessary; updating the database to reflect re-identification; updating the disease table based on the updated database; modifying thresholds of the disease markers based on the updated disease table; and identifying new disease markers based on the updated disease table.
 47. The method of claim 46 wherein identifying disease markers is based on medical information and biological data from at least about 45 patients.
 48. The method of claim 46 wherein the disease markers identify patients with disease with a specificity and a sensitivity of at least about 70%.
 49. The method of claim 46 wherein a first algorithm is used in identifying patients with disease.
 50. The method of claim 49 and further comprising: updating the first algorithm with modified thresholds, new disease markers, and by dropping disease markers.
 51. A disease identification system comprising: a database containing profiles that are clustered into disease classes based on disease identifiers; means for identifying patients with disease using a diagnostic algorithm based on the disease identifiers, the diagnostic algorithm analyzing patient profiles; means for gathering patient follow-up data indicating whether re-identification of the patients is necessary; and means for improving the diagnostic algorithm based on the patient follow-up data.
 52. The system of claim 51 wherein the means for improving the diagnostic algorithm uses an update algorithm to update a disease table and a refinement algorithm to modify thresholds of the disease identifiers, find new disease identifiers, and drop disease identifiers.
 53. The system of claim 51 wherein the profiles and the patient profiles contain demographic information, medical information, and biological data.
 54. The system of claim 51 and further comprising: means for adding the patient profiles into an identified disease class, prior to gathering patient follow-up data, to the database; and means for correcting placement of the patient profiles based on the patient follow-up data. 